One-Shot Representational Learning for Joint Biometric and Device Authentication

Sudipta Banerjee, Arun Ross

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Auto-TLDR; Joint Biometric and Device Recognition from a Single Biometric Image

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In this work, we propose a method to simultaneously perform (i) biometric recognition (\textit{i.e.}, identify the individual), and (ii) device recognition, (\textit{i.e.}, identify the device) from a single biometric image, say, a face image, using a one-shot schema. Such a joint recognition scheme can be useful in devices such as smartphones for enhancing security as well as privacy. We propose to automatically learn a joint representation that encapsulates both biometric-specific and sensor-specific features. We evaluate the proposed approach using iris, face and periocular images acquired using near-infrared iris sensors and smartphone cameras. Experiments conducted using 14,451 images from 13 sensors resulted in a rank-1 identification accuracy of upto 99.81\% and a verification accuracy of upto 100\% at a false match rate of 1\%.

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3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties

Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris, Giorgos Bouritsas, Imperial London, Sergiy Bokhnyak, Susan Walsh, Mark Shriver, Michael Bronstein, Peter Claes

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Auto-TLDR; Multi-biometric Fusion for Biometric Verification using 3D Facial Mesures

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Face recognition is a widely accepted biometric verification tool, as the face contains a lot of information about the identity of a person. In this study, a 2-step neural-based pipeline is presented for matching 3D facial shape to multiple DNA-related properties (sex, age, BMI and genomic background). The first step consists of a triplet loss-based metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest. Most studies in the field of metric learning have only focused on Euclidean data. In this work, geometric deep learning is employed to learn directly from 3D facial meshes. To this end, spiral convolutions are used along with a novel mesh-sampling scheme that retains uniformly sampled 3D points at different levels of resolution. The second step is a multi-biometric fusion by a fully connected neural network. The network takes an ensemble of embeddings and property labels as input and returns genuine and imposter scores. Since embeddings are accepted as an input, there is no need to train classifiers for the different properties and available data can be used more efficiently. Results obtained by a 10-fold cross-validation for biometric verification show that combining multiple properties leads to stronger biometric systems. Furthermore, the proposed neural-based pipeline outperforms a linear baseline, which consists of principal component analysis, followed by classification with linear support vector machines and a Naïve Bayes-based score-fuser.

Lookalike Disambiguation: Improving Face Identification Performance at Top Ranks

Thomas Swearingen, Arun Ross

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Auto-TLDR; Lookalike Face Identification Using a Disambiguator for Lookalike Images

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A face identification system compares an unknown input probe image to a gallery of face images labeled with identities in order to determine the identity of the probe image. The result of identification is a ranked match list with the most similar gallery face image at the top (rank 1) and the least similar gallery face image at the bottom. In many systems, the top ranked gallery images may look very similar to the probe image as well as to each other and can sometimes result in the misidentification of the probe image. Such similar looking faces pertaining to different identities are referred to as lookalike faces. We hypothesize that a matcher specifically trained to disambiguate lookalike face images and combined with a regular face matcher may improve overall identification performance. This work proposes reranking the initial ranked match list using a disambiguator especially for lookalike face pairs. This work also evaluates schemes to select gallery images in the initial ranked match list that should be re-ranked. Experiments on the challenging TinyFace dataset shows that the proposed approach improves the closed-set identification accuracy of a state-of-the-art face matcher.

Viability of Optical Coherence Tomography for Iris Presentation Attack Detection

Renu Sharma, Arun Ross

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Auto-TLDR; Optical Coherence Tomography Imaging for Iris Presentation Attack Detection

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In this paper, we first propose the use of Optical Coherence Tomography (OCT) imaging for the problem of iris presentation attack (PA) detection. Secondly, we assess its viability by comparing its performance with respect to traditional modalities, viz., near-infrared (NIR) and visible spectrum. OCT imaging provides a cross-sectional view of an eye, whereas NIR and visible spectrum imaging provide 2D iris textural information. Implementation is performed using three state-of-the-art deep architectures (VGG19, ResNet50 and DenseNet121) to differentiate between bonafide and PA samples for each of the three imaging modalities. Experiments are performed on a dataset of 2,169 bonafide, 177 Van Dyke eyes and 360 cosmetic contact images acquired using all three imaging modalities under intra-attack (known PAs) and cross-attack (unknown PAs) scenario. We observe promising results demonstrating OCT as a viable solution for iris PA detection.

Finger Vein Recognition and Intra-Subject Similarity Evaluation of Finger Veins Using the CNN Triplet Loss

Georg Wimmer, Bernhard Prommegger, Andreas Uhl

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Auto-TLDR; Finger vein recognition using CNNs and hard triplet online selection

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Finger vein recognition deals with the identification of subjects based on their venous pattern within the fingers. There is a lot of prior work using hand crafted features, but only little work using CNN based recognition systems. This article proposes a new approach using CNNs that utilizes the triplet loss function together with hard triplet online selection for finger vein recognition. The CNNs are used for three different use cases: (1) the classical recognition use case, where every finger of a subject is considered as a separate class, (2) an evaluation of the similarity of left and right hand fingers from the same subject and (3) an evaluation of the similarity of different fingers of the same subject. The results show that the proposed approach achieves superior results compared to prior work on finger vein recognition using the triplet loss function. Furtherly, we show that different fingers of the same subject, especially same fingers from the left and right hand, show enough similarities to perform recognition. The last statement contradicts the current understanding in the literature for finger vein biometry, in which it is assumed that different fingers of the same subject are unique identities.

SoftmaxOut Transformation-Permutation Network for Facial Template Protection

Hakyoung Lee, Cheng Yaw Low, Andrew Teoh

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Auto-TLDR; SoftmaxOut Transformation-Permutation Network for C cancellable Biometrics

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In this paper, we propose a data-driven cancellable biometrics scheme, referred to as SoftmaxOut Transformation-Permutation Network (SOTPN). The SOTPN is a neural version of Random Permutation Maxout (RPM) transform, which was introduced for facial template protection. We present a specialized SoftmaxOut layer integrated with the permutable MaxOut units and the parameterized softmax function to approximate the non-differentiable permutation and the winner-takes-all operations in the RPM transform. On top of that, a novel pairwise ArcFace loss and a code balancing loss are also formulated to ensure that the SOTPN-transformed facial template is cancellable, discriminative, high entropy and free from quantization errors when coupled with the SoftmaxOut layer. The proposed SOTPN is evaluated on three face datasets, namely LFW, YouTube Face and Facescrub, and our experimental results disclosed that the SOTPN outperforms the RPM transform significantly.

Can You Really Trust the Sensor's PRNU? How Image Content Might Impact the Finger Vein Sensor Identification Performance

Dominik Söllinger, Luca Debiasi, Andreas Uhl

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Auto-TLDR; Finger vein imagery can cause the PRNU estimate to be biased by image content

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We study the impact of highly correlated image content on the estimated sensor PRNU and its impact on the sensor identification performance. Based on eight publicly available finger vein datasets, we show formally and experimentally that the nature of finger vein imagery can cause the estimated PRNU to be biased by image content and lead to a fairly bad PRNU estimate. Such bias can cause a false increase in sensor identification performance depending on the dataset composition. Our results indicate that independent of the biometric modality, examining the quality of the estimated PRNU is essential before claiming the sensor identification performance to be good.

How Unique Is a Face: An Investigative Study

Michal Balazia, S L Happy, Francois Bremond, Antitza Dantcheva

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Auto-TLDR; Uniqueness of Face Recognition: Exploring the Impact of Factors such as image resolution, feature representation, database size, age and gender

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Face recognition has been widely accepted as a means of identification in applications ranging from border control to security in the banking sector. Surprisingly, while widely accepted, we still lack the understanding of the uniqueness or distinctiveness of face as a biometric characteristic. In this work, we study the impact of factors such as image resolution, feature representation, database size, age and gender on uniqueness denoted by the Kullback-Leibler divergence between genuine and impostor distributions. Towards understanding the impact, we present experimental results on the datasets AT&T, LFW, IMDb-Face, as well as ND-TWINS, with the feature extraction algorithms VGGFace, VGG16, ResNet50, InceptionV3, MobileNet and DenseNet121, that reveal the quantitative impact of the named factors. While these are early results, our findings indicate the need for a better understanding of the concept of biometric uniqueness and its implication on face recognition.

Multi-Level Deep Learning Vehicle Re-Identification Using Ranked-Based Loss Functions

Eleni Kamenou, Jesus Martinez-Del-Rincon, Paul Miller, Patricia Devlin - Hill

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Auto-TLDR; Multi-Level Re-identification Network for Vehicle Re-Identification

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Identifying vehicles across a network of cameras with non-overlapping fields of view remains a challenging research problem due to scene occlusions, significant inter-class similarity and intra-class variability. In this paper, we propose an end-to-end multi-level re-identification network that is capable of successfully projecting same identity vehicles closer to one another in the embedding space, compared to vehicles of different identities. Robust feature representations are obtained by combining features at multiple levels of the network. As for the learning process, we employ a recent state-of-the-art structured metric learning loss function previously applied to other retrieval problems and adjust it to the vehicle re-identification task. Furthermore, we explore the cases of image-to-image, image-to-video and video-to-video similarity metric. Finally, we evaluate our system and achieve great performance on two large-scale publicly available datasets, CityFlow-ReID and VeRi-776. Compared to most existing state-of-art approaches, our approach is simpler and more straightforward, utilizing only identity-level annotations, while avoiding post-processing the ranking results (re-ranking) at the testing phase.

DAIL: Dataset-Aware and Invariant Learning for Face Recognition

Gaoang Wang, Chen Lin, Tianqiang Liu, Mingwei He, Jiebo Luo

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Auto-TLDR; DAIL: Dataset-Aware and Invariant Learning for Face Recognition

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To achieve good performance in face recognition, a large scale training dataset is usually required. A simple yet effective way for improving the recognition performance is to use a dataset as large as possible by combining multiple datasets in the training. However, it is problematic and troublesome to naively combine different datasets due to two major issues. Firstly, the same person can possibly appear in different datasets, leading to the identity overlapping issue between different datasets. Natively treating the same person as different classes in different datasets during training will affect back-propagation and generate non-representative embeddings. On the other hand, manually cleaning labels will take a lot of human efforts, especially when there are millions of images and thousands of identities. Secondly, different datasets are collected in different situations and thus will lead to different domain distributions. Natively combining datasets will lead to domain distribution differences and make it difficult to learn domain invariant embeddings across different datasets. In this paper, we propose DAIL: Dataset-Aware and Invariant Learning to resolve the above-mentioned issues. To solve the first issue of identity overlapping, we propose a dataset-aware loss for multi-dataset training by reducing the penalty when the same person appears in multiple datasets. This can be readily achieved with a modified softmax loss with a dataset-aware term. To solve the second issue, the domain adaptation with gradient reversal layers is employed for dataset invariant learning. The proposed approach not only achieves state-of-the-art results on several commonly used face recognition validation sets, like LFW, CFP-FP, AgeDB-30, but also shows great benefit for practical usage.

Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings

Mehak Gupta, Vishal Singh, Akshay Agarwal, Mayank Vatsa, Richa Singh

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Auto-TLDR; MVNet: A Deep Learning-based PAD Network for Iris Recognition against Presentation Attacks

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The deployment of biometrics features based person identification has increased significantly from border access to mobile unlock to electronic transactions. Iris recognition is considered as one of the most accurate biometric modality for person identification. However, the vulnerability of this recognition towards presentation attacks, especially towards the 3D contact lenses, can limit its potential deployments. The textured lenses are so effective in hiding the real texture of iris that it can fool not only the automatic recognition algorithms but also the human examiners. While in literature, several presentation attack detection (PAD) algorithms are presented; however, the significant limitation is the generalizability against an unseen database, unseen sensor, and different imaging environment. Inspired by the success of the hybrid algorithm or fusion of multiple detection networks, we have proposed a deep learning-based PAD network that utilizes multiple feature representation layers. The computational complexity is an essential factor in training the deep neural networks; therefore, to limit the computational complexity while learning multiple feature representation layers, a base model is kept the same. The network is trained end-to-end using a softmax classifier. We have evaluated the performance of the proposed network termed as MVNet using multiple databases such as IIITD-WVU MUIPA, IIITD-WVU UnMIPA database under cross-database training-testing settings. The experiments are performed extensively to assess the generalizability of the proposed algorithm.

Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches

Kalun Ho, Janis Keuper, Franz-Josef Pfreundt, Margret Keuper

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Auto-TLDR; Clustering Objectives for K-means and Correlation Clustering Using Triplet Loss

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In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.

Total Whitening for Online Signature Verification Based on Deep Representation

Xiaomeng Wu, Akisato Kimura, Kunio Kashino, Seiichi Uchida

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Auto-TLDR; Total Whitening for Online Signature Verification

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In deep metric learning targeted at time series, the correlation between feature activations may be easily enlarged through highly nonlinear neural networks, leading to suboptimal embedding effectiveness. An effective solution to this problem is whitening. For example, in online signature verification, whitening can be derived for three individual Gaussian distributions, namely the distributions of local features at all temporal positions 1) for all signatures of all subjects, 2) for all signatures of each particular subject, and 3) for each particular signature of each particular subject. This study proposes a unified method called total whitening that integrates these individual Gaussians. Total whitening rectifies the layout of multiple individual Gaussians to resemble a standard normal distribution, improving the balance between intraclass invariance and interclass discriminative power. Experimental results demonstrate that total whitening achieves state-of-the-art accuracy when tested on online signature verification benchmarks.

SSDL: Self-Supervised Domain Learning for Improved Face Recognition

Samadhi Poornima Kumarasinghe Wickrama Arachchilage, Ebroul Izquierdo

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Auto-TLDR; Self-supervised Domain Learning for Face Recognition in unconstrained environments

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Face recognition in unconstrained environments is challenging due to variations in illumination, quality of sensing, motion blur and etc. An individual’s face appearance can vary drastically under different conditions creating a gap between train (source) and varying test (target) data. The domain gap could cause decreased performance levels in direct knowledge transfer from source to target. Despite fine-tuning with domain specific data could be an effective solution, collecting and annotating data for all domains is extremely expensive. To this end, we propose a self-supervised domain learning (SSDL) scheme that trains on triplets mined from unlabelled data. A key factor in effective discriminative learning, is selecting informative triplets. Building on most confident predictions, we follow an “easy-to-hard” scheme of alternate triplet mining and self-learning. Comprehensive experiments on four different benchmarks show that SSDL generalizes well on different domains.

Cancelable Biometrics Vault: A Secure Key-Binding Biometric Cryptosystem Based on Chaffing and Winnowing

Osama Ouda, Karthik Nandakumar, Arun Ross

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Auto-TLDR; Cancelable Biometrics Vault for Key-binding Biometric Cryptosystem Framework

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Existing key-binding biometric cryptosystems, such as the Fuzzy Vault Scheme (FVS) and Fuzzy Commitment Scheme (FCS), employ Error Correcting Codes (ECC) to handle intra-user variations in biometric data. As a result, a trade-off exists between the key length and matching accuracy. Moreover, these systems are vulnerable to privacy leakage, i.e., it is trivial to recover the original biometric template given the secure sketch and its associated cryptographic key. In this work, we propose a novel key-binding biometric cryptosystem framework, referred to as Cancelable Biometrics Vault (CBV), to address the above two limitations. The CBV framework is inspired by the cryptographic principle of chaffing and winnowing. It utilizes the concept of cancelable biometrics (CB) to generate secure biometric templates, which in turn are used to encode bits in a cryptographic key. While the CBV framework is generic and does not rely on a specific biometric representation, it does assume the availability of a suitable (satisfying the requirements of accuracy preservation, non-invertibility, and non-linkability) CB scheme for the given representation. To demonstrate the usefulness of the proposed CBV framework, we implement this approach using an extended BioEncoding scheme, which is a CB scheme appropriate for bit strings such as iris-codes. Unlike the baseline BioEncoding scheme, the extended version proposed in this work fulfills all the three requirements of a CB construct. Experiments show that the decoding accuracy of the proposed CBV framework is comparable to the recognition accuracy of the underlying CB construct, namely, the extended BioEncoding scheme, regardless of the cryptographic key size.

Learning Metric Features for Writer-Independent Signature Verification Using Dual Triplet Loss

Qian Wan, Qin Zou

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Auto-TLDR; A dual triplet loss based method for offline writer-independent signature verification

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Handwritten signature has long been a widely accepted biometric and applied in many verification scenarios. However, automatic signature verification remains an open research problem, which is mainly due to three reasons. 1) Skilled forgeries generated by persons who imitate the original writting pattern are very difficult to be distinguished from genuine signatures. It is especially so in the case of offline signatures, where only the signature image is captured as a feature for verification. 2) Most state-of-the-art models are writer-dependent, requiring a specific model to be trained whenever a new user is registered in verification, which is quite inconvenient. 3) Writer-independent models often have unsatisfactory performance. To this end, we propose a novel metric learning based method for offline writer-independent signature verification. Specifically, a dual triplet loss is used to train the model, where two different triplets are constructed for random and skilled forgeries, respectively. Experiments on three alphabet datasets — GPDS Synthetic, MCYT and CEDAR — show that the proposed method achieves competitive or superior performance to the state-of-the-art methods. Experiments are also conducted on a new offline Chinese signature dataset — CSIG-WHU, and the results show that the proposed method has a high feasibility on character-based signatures.

ClusterFace: Joint Clustering and Classification for Set-Based Face Recognition

Samadhi Poornima Kumarasinghe Wickrama Arachchilage, Ebroul Izquierdo

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Auto-TLDR; Joint Clustering and Classification for Face Recognition in the Wild

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Deep learning technology has enabled successful modeling of complex facial features when high quality images are available. Nonetheless, accurate modeling and recognition of human faces in real world scenarios 'on the wild' or under adverse conditions remains an open problem. When unconstrained faces are mapped into deep features, variations such as illumination, pose, occlusion, etc., can create inconsistencies in the resultant feature space. Hence, deriving conclusions based on direct associations could lead to degraded performance. This rises the requirement for a basic feature space analysis prior to face recognition. This paper devises a joint clustering and classification scheme which learns deep face associations in an easy-to-hard way. Our method is based on hierarchical clustering where the early iterations tend to preserve high reliability. The rationale of our method is that a reliable clustering result can provide insights on the distribution of the feature space, that can guide the classification that follows. Experimental evaluations on three tasks, face verification, face identification and rank-order search, demonstrates better or competitive performance compared to the state-of-the-art, on all three experiments.

Loop-closure detection by LiDAR scan re-identification

Jukka Peltomäki, Xingyang Ni, Jussi Puura, Joni-Kristian Kamarainen, Heikki Juhani Huttunen

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Auto-TLDR; Loop-Closing Detection from LiDAR Scans Using Convolutional Neural Networks

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In this work, loop-closure detection from LiDAR scans is defined as an image re-identification problem. Re-identification is performed by computing Euclidean distances of a query scan to a gallery set of previous scans. The distances are computed in a feature embedding space where the scans are mapped by a convolutional neural network (CNN). The network is trained using the triplet loss training strategy. In our experiments we compare different backbone networks, variants of the triplet loss and generic and LiDAR specific data augmentation techniques. With a realistic indoor dataset the best architecture obtains the mean average precision (mAP) above 90%.

SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition

Raphael Memmesheimer, Nick Theisen, Dietrich Paulus

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Auto-TLDR; One-Shot Action Recognition using Metric Learning

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Recognizing an activity with a single reference sample using metric learning approaches is a promising research field. The majority of few-shot methods focus on object recognition or face-identification. We propose a metric learning approach to reduce the action recognition problem to a nearest neighbor search in embedding space. We encode signals into images and extract features using a deep residual CNN. Using triplet loss, we learn a feature embedding. The resulting encoder transforms features into an embedding space in which closer distances encode similar actions while higher distances encode different actions. Our approach is based on a signal level formulation and remains flexible across a variety of modalities. It further outperforms the baseline on the large scale NTU RGB+D 120 dataset for the One-Shot action recognition protocol by \ntuoneshotimpro%. With just 60% of the training data, our approach still outperforms the baseline approach by \ntuoneshotimproreduced%. With 40% of the training data, our approach performs comparably well as the second follow up. Further, we show that our approach generalizes well in experiments on the UTD-MHAD dataset for inertial, skeleton and fused data and the Simitate dataset for motion capturing data. Furthermore, our inter-joint and inter-sensor experiments suggest good capabilities on previously unseen setups.

Super-Resolution Guided Pore Detection for Fingerprint Recognition

Syeda Nyma Ferdous, Ali Dabouei, Jeremy Dawson, Nasser M. Nasarabadi

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Auto-TLDR; Super-Resolution Generative Adversarial Network for Fingerprint Recognition Using Pore Features

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Performance of fingerprint recognition algorithms substantially rely on fine features extracted from fingerprints. Apart from minutiae and ridge patterns, pore features have proven to be usable for fingerprint recognition. Although features from minutiae and ridge patterns are quite attainable from low-resolution images, using pore features is practical only if the fingerprint image is of high resolution which necessitates a model that enhances the image quality of the conventional 500 ppi legacy fingerprints preserving the fine details. To find a solution for recovering pore information from low-resolution fingerprints, we adopt a joint learning-based approach that combines both super-resolution and pore detection networks. Our modified single image Super-Resolution Generative Adversarial Network (SRGAN) framework helps to reliably reconstruct high-resolution fingerprint samples from low-resolution ones assisting the pore detection network to identify pores with a high accuracy. The network jointly learns a distinctive feature representation from a real low-resolution fingerprint sample and successfully synthesizes a high-resolution sample from it. To add discriminative information and uniqueness for all the subjects, we have integrated features extracted from a deep fingerprint verifier with the SRGAN quality discriminator. We also add ridge reconstruction loss, utilizing ridge patterns to make the best use of extracted features. Our proposed method solves the recognition problem by improving the quality of fingerprint images. High recognition accuracy of the synthesized samples that is close to the accuracy achieved using the original high-resolution images validate the effectiveness of our proposed model.

Rotation Invariant Aerial Image Retrieval with Group Convolutional Metric Learning

Hyunseung Chung, Woo-Jeoung Nam, Seong-Whan Lee

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Auto-TLDR; Robust Remote Sensing Image Retrieval Using Group Convolution with Attention Mechanism and Metric Learning

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Remote sensing image retrieval (RSIR) is the process of ranking database images depending on the degree of similarity compared to the query image. As the complexity of RSIR increases due to the diversity in shooting range, angle, and location of remote sensors, there is an increasing demand for methods to address these issues and improve retrieval performance. In this work, we introduce a novel method for retrieving aerial images by merging group convolution with attention mechanism and metric learning, resulting in robustness to rotational variations. For refinement and emphasis on important features, we applied channel attention in each group convolution stage. By utilizing the characteristics of group convolution and channel-wise attention, it is possible to acknowledge the equality among rotated but identically located images. The training procedure has two main steps: (i) training the network with Aerial Image Dataset (AID) for classification, (ii) fine-tuning the network with triplet-loss for retrieval with Google Earth South Korea and NWPU-RESISC45 datasets. Results show that the proposed method performance exceeds other state-of-the-art retrieval methods in both rotated and original environments. Furthermore, we utilize class activation maps (CAM) to visualize the distinct difference of main features between our method and baseline, resulting in better adaptability in rotated environments.

Generalized Local Attention Pooling for Deep Metric Learning

Carlos Roig Mari, David Varas, Issey Masuda, Juan Carlos Riveiro, Elisenda Bou-Balust

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Auto-TLDR; Generalized Local Attention Pooling for Deep Metric Learning

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Deep metric learning has been key to recent advances in face verification and image retrieval amongst others. These systems consist on a feature extraction block (extracts feature maps from images) followed by a spatial dimensionality reduction block (generates compact image representations from the feature maps) and an embedding generation module (projects the image representation to the embedding space). While research on deep metric learning has focused on improving the losses for the embedding generation module, the dimensionality reduction block has been overlooked. In this work, we propose a novel method to generate compact image representations which uses local spatial information through an attention mechanism, named Generalized Local Attention Pooling (GLAP). This method, instead of being placed at the end layer of the backbone, is connected at an intermediate level, resulting in lower memory requirements. We assess the performance of the aforementioned method by comparing it with multiple dimensionality reduction techniques, demonstrating the importance of using attention weights to generate robust compact image representations. Moreover, we compare the performance of multiple state-of-the-art losses using the standard deep metric learning system against the same experiment with our GLAP. Experiments showcase that the proposed Generalized Local Attention Pooling mechanism outperforms other pooling methods when compared with current state-of-the-art losses for deep metric learning.

A Cross Domain Multi-Modal Dataset for Robust Face Anti-Spoofing

Qiaobin Ji, Shugong Xu, Xudong Chen, Shan Cao, Shunqing Zhang

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Auto-TLDR; Cross domain multi-modal FAS dataset GREAT-FASD and several evaluation protocols for academic community

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Face Anti-spoofing (FAS) is a challenging problem due to the complex serving scenario and diverse face presentation attack patterns. Using single modal images which are usually captured with RGB cameras is not able to deal with the former because of serious overfitting problems. The existing multi-modal FAS datasets rarely pay attention to the cross domain problems, trainingFASsystemonthesedataleadstoinconsistenciesandlow generalization capabilities in deployment since imaging principles(structured light, TOF, etc.) and pre-processing methods vary between devices. We explore the subtle fine-grained differences betweeen multi-modal cameras and proposed a cross domain multi-modal FAS dataset GREAT-FASD and several evaluation protocols for academic community. Furthermore, we incorporate the multiplicative attention and center loss to enhance the representative power of CNN via seeking out complementary information as a powerful baseline. In addition, extensive experiments have been conducted on the proposed dataset to analyze the robustness to distinguish spoof faces and bona-fide faces. Experimental results show the effectiveness of proposed method and achieve the state-of-the-art competitive results. Finally, we visualize our future distribution in hidden space and observe that the proposed method is able to lead the network to generate a large margin for face anti-spoofing task

Cam-Softmax for Discriminative Deep Feature Learning

Tamas Suveges, Stephen James Mckenna

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Auto-TLDR; Cam-Softmax: A Generalisation of Activations and Softmax for Deep Feature Spaces

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Deep convolutional neural networks are widely used to learn feature spaces for image classification tasks. We propose cam-softmax, a generalisation of the final layer activations and softmax function, that encourages deep feature spaces to exhibit high intra-class compactness and high inter-class separability. We provide an algorithm to automatically adapt the method's main hyperparameter so that it gradually diverges from the standard activations and softmax method during training. We report experiments using CASIA-Webface, LFW, and YTF face datasets demonstrating that cam-softmax leads to representations well suited to open-set face recognition and face pair matching. Furthermore, we provide empirical evidence that cam-softmax provides some robustness to class labelling errors in training data, making it of potential use for deep learning from large datasets with poorly verified labels.

Lightweight Low-Resolution Face Recognition for Surveillance Applications

Yoanna Martínez-Díaz, Heydi Mendez-Vazquez, Luis S. Luevano, Leonardo Chang, Miguel Gonzalez-Mendoza

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Auto-TLDR; Efficiency of Lightweight Deep Face Networks on Low-Resolution Surveillance Imagery

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Typically, real-world requirements to deploy face recognition models in unconstrained surveillance scenarios demand to identify low-resolution faces with extremely low computational cost. In the last years, several methods based on complex deep learning models have been proposed with promising recognition results but at a high computational cost. Inspired by the compactness and computation efficiency of lightweight deep face networks and their high accuracy on general face recognition tasks, in this work we propose to benchmark two recently introduced lightweight face models on low-resolution surveillance imagery to enable efficient system deployment. In this way, we conduct a comprehensive evaluation on the two typical settings: LR-to-HR and LR-to-LR matching. In addition, we investigate the effect of using trained models with down-sampled synthetic data from high-resolution images, as well as the combination of different models, for face recognition on real low-resolution images. Experimental results show that the used lightweight face models achieve state-of-the-art results on low-resolution benchmarks with low memory footprint and computational complexity. Moreover, we observed that combining models trained with different degradations improves the recognition accuracy on low-resolution surveillance imagery, which is feasible due to their low computational cost.

Fingerprints, Forever Young?

Roman Kessler, Olaf Henniger, Christoph Busch

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Auto-TLDR; Mated Similarity Scores for Fingerprint Recognition: A Hierarchical Linear Model

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In the present study we analyzed longitudinal fingerprint data of 20 data subjects, acquired over a time span of up to 12 years. Using hierarchical linear modeling, we aimed to delineate mated similarity scores as a function of fingerprint quality and of the time interval between reference and probe images. Our results did not reveal effects on mated similarity scores caused by an increasing time interval across subjects, but rather individual effects on mated similarity scores. The results are in line with the general assumption that the fingerprint as a biometric characteristic and the features extracted from it do not change over the adult life span. However, it contradicts several related studies that reported noticeable template ageing effects. We discuss why different findings regarding ageing of references in fingerprint recognition systems were made.

Angular Sparsemax for Face Recognition

Chi Ho Chan, Josef Kittler

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Auto-TLDR; Angular Sparsemax for Face Recognition

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We formulate a novel loss function, called Angular Sparsemax for face recognition. The proposed loss function promotes sparseness of the hypotheses prediction function similar to Sparsemax with Fenchel-Young regularisation. With introducing an additive angular margin on the score vector, the discriminatory power of the face embedding is further improved. The proposed loss function is experimentally validated on several databases in term of recognition accuracy. Its performance compares well with the state of the art Arcface loss.

Deep Top-Rank Counter Metric for Person Re-Identification

Chen Chen, Hao Dou, Xiyuan Hu, Silong Peng

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Auto-TLDR; Deep Top-Rank Counter Metric for Person Re-identification

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In the research field of person re-identification, deep metric learning that guides the efficient and effective embedding learning serves as one of the most fundamental tasks. Recent efforts of the loss function based deep metric learning methods mainly focus on the top rank accuracy optimization by minimiz- ing the distance difference between the correctly matching sample pair and wrongly matched sample pair. However, it is more straightforward to count the occurrences of correct top-rank candidates and maximize the counting results for better top rank accuracy. In this paper, we propose a generalized logistic function based metric with effective practicalness in deep learning, namely the“deep top-rank counter metric”, to approximately optimize the counted occurrences of the correct top-rank matches. The properties that qualify the proposed metric as a well-suited deep re-identification metric have been discussed and a progressive hard sample mining strategy is also introduced for effective training and performance boosting. The extensive experiments show that the proposed top-rank counter metric outperforms other loss function based deep metrics and achieves the state-of- the-art accuracies.

Age Gap Reducer-GAN for Recognizing Age-Separated Faces

Daksha Yadav, Naman Kohli, Mayank Vatsa, Richa Singh, Afzel Noore

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Auto-TLDR; Generative Adversarial Network for Age-separated Face Recognition

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In this paper, we propose a novel algorithm for matching faces with temporal variations caused due to age progression. The proposed generative adversarial network algorithm is a unified framework which combines facial age estimation and age-separated face verification. The key idea of this approach is to learn the age variations across time by conditioning the input image on the subject's gender and the target age group to which the face needs to be progressed. The loss function accounts for reducing the age gap between the original image and generated face image as well as preserving the identity. Both visual fidelity and quantitative evaluations demonstrate the efficacy of the proposed architecture on different facial age databases for age-separated face recognition.

Cut and Compare: End-To-End Offline Signature Verification Network

Xi Lu, Lin-Lin Huang, Fei Yin

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Auto-TLDR; An End-to-End Cut-and-Compare Network for Offline Signature Verification

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Offline signature verification, to determine whether a handwritten signature image is genuine or forged for a claimed identity, is needed in many applications. How to extract salient features and how to calculate similarity scores are the major issues. In this paper, we propose a novel end-to-end cut-and-compare network for offline signature verification. Based on the Spatial Transformer Network (STN), discriminative regions are segmented from a pair of input signature images and are compared attentively with help of Attentive Recurrent Comparator (ARC). An adaptive distance fusion module is proposed to fuse the distances of these regions. To address the intrapersonal variability problem, we design a smoothed double-margin loss to train the network. The proposed network achieves state-of-the-art performance on CEDAR, GPDS Synthetic, BHSig-H and BHSig-B datasets of different languages. Furthermore, our network shows strong generalization ability on cross-language test.

Identifying Missing Children: Face Age-Progression Via Deep Feature Aging

Debayan Deb, Divyansh Aggarwal, Anil Jain

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Auto-TLDR; Aging Face Features for Missing Children Identification

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Given a face image of a recovered child at probe-age, we search a gallery of missing children with known identities and gallery-ages at which they were either lost or stolen in an attempt to unite the recovered child with his family. We propose a feature aging module that can age-progress deep face features output by a face matcher to improve the recognition accuracy of age-separated child face images. In addition, the feature aging module guides age-progression in the image space such that synthesized aged gallery faces can be utilized to further enhance cross-age face matching accuracy of any commodity face matcher. For time lapses larger than 10 years (the missing child is recovered after 10 or more years), the proposed age-progression module improves the closed-set identification accuracy of CosFace from 60.72% to 66.12% on a child celebrity dataset, namely ITWCC. The proposed method also outperforms state-of-the-art approaches with a rank-1 identification rate of 95.91%, compared to 94.91%, on a public aging dataset, FG-NET, and 99.58%, compared to 99.50%, on CACD-VS. These results suggest that aging face features enhances the ability to identify young children who are possible victims of child trafficking or abduction.

Nonlinear Ranking Loss on Riemannian Potato Embedding

Byung Hyung Kim, Yoonje Suh, Honggu Lee, Sungho Jo

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Auto-TLDR; Riemannian Potato for Rank-based Metric Learning

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We propose a rank-based metric learning method by leveraging a concept of the Riemannian Potato for better separating non-linear data. By exploring the geometric properties of Riemannian manifolds, the proposed loss function optimizes the measure of dispersion using the distribution of Riemannian distances between a reference sample and neighbors and builds a ranked list according to the similarities. We show the proposed function can learn a hypersphere for each class, preserving the similarity structure inside it on Riemannian manifold. As a result, compared with Euclidean distance-based metric, our method can further jointly reduce the intra-class distances and enlarge the inter-class distances for learned features, consistently outperforming state-of-the-art methods on three widely used non-linear datasets.

RGB-Infrared Person Re-Identification Via Image Modality Conversion

Huangpeng Dai, Qing Xie, Yanchun Ma, Yongjian Liu, Shengwu Xiong

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Auto-TLDR; CE2L: A Novel Network for Cross-Modality Re-identification with Feature Alignment

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As a cross modality retrieval task, RGB-infrared person re-identification(Re-ID) is an important and challenging tasking, because of its important role in video surveillance applications and large cross-modality variations between visible and infrared images. Most previous works addressed the problem of cross-modality gap with feature alignment by original feature representation learning straightly. In this paper, different from existing works, we propose a novel network(CE2L) to tackle the cross-modality gap with feature alignment. CE2L mainly focuses on adding discriminative information and learning robust features by converting modality between visible and infrared images. Its merits are highlighted in two aspects: 1)Using CycleGAN to convert infrared images into color images can not only increase the recognition characteristics of images, but also allow the our network to better learn the two modal image features; 2)Our novel method can serve as data augmentation. Specifically, it can increase data diversity and total data against over-fitting by converting labeled training images to another modal images. Extensive experimental results on two datasets demonstrate superior performance compared to the baseline and the state-of-the-art methods.

Learning Disentangled Representations for Identity Preserving Surveillance Face Camouflage

Jingzhi Li, Lutong Han, Hua Zhang, Xiaoguang Han, Jingguo Ge, Xiaochu Cao

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Auto-TLDR; Individual Face Privacy under Surveillance Scenario with Multi-task Loss Function

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In this paper, we focus on protecting the person face privacy under the surveillance scenarios, whose goal is to change the visual appearances of faces while keep them to be recognizable by current face recognition systems. This is a challenging problem as that we should retain the most important structures of captured facial images, while alter the salient facial regions to protect personal privacy. To address this problem, we introduce a novel individual face protection model, which can camouflage the face appearance from the perspective of human visual perception and preserve the identity features of faces used for face authentication. To that end, we develop an encoder-decoder network architecture that can separately disentangle the person feature representation into an appearance code and an identity code. Specifically, we first randomly divide the face image into two groups, the source set and the target set, where the source set is used to extract the identity code and the target set provides the appearance code. Then, we recombine the identity and appearance codes to synthesize a new face, which has the same identity with the source subject. Finally, the synthesized faces are used to replace the original face to protect the privacy of individual. Furthermore, our model is trained end-to-end with a multi-task loss function, which can better preserve the identity and stabilize the training loss. Experiments conducted on Cross-Age Celebrity dataset demonstrate the effectiveness of our model and validate our superiority in terms of visual quality and scalability.

Siamese Graph Convolution Network for Face Sketch Recognition

Liang Fan, Xianfang Sun, Paul Rosin

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Auto-TLDR; A novel Siamese graph convolution network for face sketch recognition

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In this paper, we present a novel Siamese graph convolution network (GCN) for face sketch recognition. To build a graph from an image, we utilize a deep learning method to detect the image edges, and then use a superpixel method to segment the edge image. Each segmented superpixel region is taken as a node, and each pair of adjacent regions forms an edge of the graph. Graphs from both a face sketch and a face photo are input into the Siamese GCN for recognition. A deep graph matching method is used to share messages between cross-modal graphs in this model. Experiments show that the GCN can obtain high performance on several face photo-sketch datasets, including seen and unseen face photo-sketch datasets. It is also shown that the model performance based on the graph structure representation of the data using the Siamese GCN is more stable than a Siamese CNN model.

Cross-spectrum Face Recognition Using Subspace Projection Hashing

Hanrui Wang, Xingbo Dong, Jin Zhe, Jean-Luc Dugelay, Massimo Tistarelli

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Auto-TLDR; Subspace Projection Hashing for Cross-Spectrum Face Recognition

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Cross-spectrum face recognition, e.g. visible to thermal matching, remains a challenging task due to the large variation originated from different domains. This paper proposed a subspace projection hashing (SPH) to enable the cross-spectrum face recognition task. The intrinsic idea behind SPH is to project the features from different domains onto a common subspace, where matching the faces from different domains can be accomplished. Notably, we proposed a new loss function that can (i) preserve both inter-domain and intra-domain similarity; (ii) regularize a scaled-up pairwise distance between hashed codes, to optimize projection matrix. Three datasets, Wiki, EURECOM VIS-TH paired face and TDFace are adopted to evaluate the proposed SPH. The experimental results indicate that the proposed SPH outperforms the original linear subspace ranking hashing (LSRH) in the benchmark dataset (Wiki) and demonstrates a reasonably good performance for visible-thermal, visible-near-infrared face recognition, therefore suggests the feasibility and effectiveness of the proposed SPH.

Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification

Can Zhang, Hong Liu, Wei Guo, Mang Ye

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Auto-TLDR; Multi-Scale Part-Aware Cascading for RGB-Infrared Person Re-identification

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RGB-Infrared person re-identification (RGB-IR Re-ID) aims to matching persons from heterogeneous images captured by visible and thermal cameras, which is of great significance in surveillance system under poor light conditions. Facing great challenges in complex variances including conventional single-modality and additional inter-modality discrepancies, most of existing RGB-IR Re-ID methods directly work on global features for simultaneous elimination, whereas modality-specific noises and modality-shared features are not well considered. To address these issues, a novel Multi-Scale Part-Aware Cascading framework (MSPAC) is formulated by aggregating multi-scale fine-grained features from part to global in a cascading manner, which results in an unified representation robust to noises. Moreover, a marginal exponential center (MeCen) loss is introduced to jointly eliminate mixed variances, which enables to model cross-modality correlations on sharable salient features. Extensive experiments are conducted for demonstration that the proposed method outperforms all the state-of-the-arts by a large margin.

Deep Gait Relative Attribute Using a Signed Quadratic Contrastive Loss

Yuta Hayashi, Shehata Allam, Yasushi Makihara, Daigo Muramatsu, Yasushi Yagi

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Auto-TLDR; Signal-Contrastive Loss for Gait Attributes Estimation

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This paper presents a deep learning-based method to estimate gait attributes (e.g., stately, cool, relax, etc.). Similarly to the existing studies on relative attribute, human perception-based annotations on the gait attributes are given to pairs of gait videos (i.e., the first one is better, tie, and the second one is better), and the relative annotations are utilized to train a ranking model of the gait attribute. More specifically, we design a Siamese (i.e., two-stream) network which takes a pair of gait inputs and output gait attribute score for each. We then introduce a suitable loss function called a signed contrastive loss to train the network parameters with the relative annotation. Unlike the existing loss functions for learning to rank does not inherent a nice property of a quadratic contrastive loss, the proposed signed quadratic contrastive loss function inherents the nice property. The quantitative evaluation results reveal that the proposed method shows better or comparable accuracies of relative attribute prediction against the baseline methods.

Face Image Quality Assessment for Model and Human Perception

Ken Chen, Yichao Wu, Zhenmao Li, Yudong Wu, Ding Liang

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Auto-TLDR; A labour-saving method for FIQA training with contradictory data from multiple sources

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Practical face image quality assessment (FIQA) models are trained under the supervision of labeled data, which requires more or less human labor. The human labeled quality scores are consistent with perceptual intuition but laborious. On the other hand, models can be trained with data generated automatically by the recognition models with artificially selected references. However, the recognition scores are sometimes inaccurate, which may give wrong quality scores during FIQA training. In this paper, we propose a labour-saving method for quality scores generation. For the first time, we conduct systematic investigations to show that there exist severe contradictions between different types of target quality, namely distribution gap (DG). To bridge the gap, we propose a novel framework for training FIQA models by combining the merits of data from different sources. In order to make the target score from multiple sources compatible, we design a method called quality distribution alignment (QDA). Meanwhile, to correct the wrong target by recognition models, contradictory samples selection (CSS) is adopted to select samples from the human labeled dataset adaptively. Extensive experiments and analysis on public benchmarks including MegaFace has demonstrated the superiority of our in terms of effectiveness and efficiency.

Detection of Makeup Presentation Attacks Based on Deep Face Representations

Christian Rathgeb, Pawel Drozdowski, Christoph Busch

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Auto-TLDR; An Attack Detection Scheme for Face Recognition Using Makeup Presentation Attacks

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Facial cosmetics have the ability to substantially alter the facial appearance, which can negatively affect the decisions of a face recognition. In addition, it was recently shown that the application of makeup can be abused to launch so-called makeup presentation attacks. In such attacks, the attacker might apply heavy makeup in order to achieve the facial appearance of a target subject for the purpose of impersonation. In this work, we assess the vulnerability of a COTS face recognition system to makeup presentation attacks employing the publicly available Makeup Induced Face Spoofing (MIFS) database. It is shown that makeup presentation attacks might seriously impact the security of the face recognition system. Further, we propose an attack detection scheme which distinguishes makeup presentation attacks from genuine authentication attempts by analysing differences in deep face representations obtained from potential makeup presentation attacks and corresponding target face images. The proposed detection system employs a machine learning-based classifier, which is trained with synthetically generated makeup presentation attacks utilizing a generative adversarial network for facial makeup transfer in conjunction with image warping. Experimental evaluations conducted using the MIFS database reveal a detection equal error rate of 0.7% for the task of separating genuine authentication attempts from makeup presentation attacks.

Two-Level Attention-Based Fusion Learning for RGB-D Face Recognition

Hardik Uppal, Alireza Sepas-Moghaddam, Michael Greenspan, Ali Etemad

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Auto-TLDR; Fused RGB-D Facial Recognition using Attention-Aware Feature Fusion

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With recent advances in RGB-D sensing technologies as well as improvements in machine learning and fusion techniques, RGB-D facial recognition has become an active area of research. A novel attention aware method is proposed to fuse two image modalities, RGB and depth, for enhanced RGB-D facial recognition. The proposed method first extracts features from both modalities using a convolutional feature extractor. These features are then fused using a two layer attention mechanism. The first layer focuses on the fused feature maps generated by the feature extractor, exploiting the relationship between feature maps using LSTM recurrent learning. The second layer focuses on the spatial features of those maps using convolution. The training database is preprocessed and augmented through a set of geometric transformations, and the learning process is further aided using transfer learning from a pure 2D RGB image training process. Comparative evaluations demonstrate that the proposed method outperforms other state-of-the-art approaches, including both traditional and deep neural network-based methods, on the challenging CurtinFaces and IIIT-D RGB-D benchmark databases, achieving classification accuracies over 98.2% and 99.3% respectively. The proposed attention mechanism is also compared with other attention mechanisms, demonstrating more accurate results.

Attention-Based Deep Metric Learning for Near-Duplicate Video Retrieval

Kuan-Hsun Wang, Chia Chun Cheng, Yi-Ling Chen, Yale Song, Shang-Hong Lai

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Auto-TLDR; Attention-based Deep Metric Learning for Near-duplicate Video Retrieval

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Near-duplicate video retrieval (NDVR) is an important and challenging problem due to the increasing amount of videos uploaded to the Internet. In this paper, we propose an attention-based deep metric learning method for NDVR. Our method is based on well-established principles: We leverage two-stream networks to combine RGB and optical flow features, and incorporate an attention module to effectively deal with distractor frames commonly observed in near duplicate videos. We further aggregate the features corresponding to multiple video segments to enhance the discriminative power. The whole system is trained using a deep metric learning objective with a Siamese architecture. Our experiments show that the attention module helps eliminate redundant and noisy frames, while focusing on visually relevant frames for solving NVDR. We evaluate our approach on recent large-scale NDVR datasets, CC_WEB_VIDEO, VCDB, FIVR and SVD. To demonstrate the generalization ability of our approach, we report results in both within- and cross-dataset settings, and show that the proposed method significantly outperforms state-of-the-art approaches.

Quality-Based Representation for Unconstrained Face Recognition

Nelson Méndez-Llanes, Katy Castillo-Rosado, Heydi Mendez-Vazquez, Massimo Tistarelli

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Auto-TLDR; activation map for face recognition in unconstrained environments

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Significant advances have been achieved in face recognition in the last decade thanks to the development of deep learning methods. However, recognizing faces captured in uncontrolled environments is still a challenging problem for the scientific community. In these scenarios, the performance of most of existing deep learning based methods abruptly falls, due to the bad quality of the face images. In this work, we propose to use an activation map to represent the quality information in a face image. Different face regions are analyzed to determine their quality and then only those regions with good quality are used to perform the recognition using a given deep face model. For experimental evaluation, in order to simulate unconstrained environments, three challenging databases, with different variations in appearance, were selected: the Labeled Faces in the Wild Database, the Celebrities in Frontal-Profile in the Wild Database, and the AR Database. Three deep face models were used to evaluate the proposal on these databases and in all cases, the use of the proposed activation map allows the improvement of the recognition rates obtained by the original models in a range from 0.3 up to 31%. The obtained results experimentally demonstrated that the proposal is able to select those face areas with higher discriminative power and enough identifying information, while ignores the ones with spurious information.

Are Spoofs from Latent Fingerprints a Real Threat for the Best State-Of-Art Liveness Detectors?

Roberto Casula, Giulia Orrù, Daniele Angioni, Xiaoyi Feng, Gian Luca Marcialis, Fabio Roli

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Auto-TLDR; ScreenSpoof: Attacks using latent fingerprints against state-of-art fingerprint liveness detectors and verification systems

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We investigated the threat level of realistic attacks using latent fingerprints against sensors equipped with state-of-art liveness detectors and fingerprint verification systems which integrate such liveness algorithms. To the best of our knowledge, only a previous investigation was done with spoofs from latent prints. In this paper, we focus on using snapshot pictures of latent fingerprints. These pictures provide molds, that allows, after some digital processing, to fabricate high-quality spoofs. Taking a snapshot picture is much simpler than developing fingerprints left on a surface by magnetic powders and lifting the trace by a tape. What we are interested here is to evaluate preliminary at which extent attacks of the kind can be considered a real threat for state-of-art fingerprint liveness detectors and verification systems. To this aim, we collected a novel data set of live and spoof images fabricated with snapshot pictures of latent fingerprints. This data set provide a set of attacks at the most favourable conditions. We refer to this method and the related data set as "ScreenSpoof". Then, we tested with it the performances of the best liveness detection algorithms, namely, the three winners of the LivDet competition. Reported results point out that the ScreenSpoof method is a threat of the same level, in terms of detection and verification errors, than that of attacks using spoofs fabricated with the full consensus of the victim. We think that this is a notable result, never reported in previous work.

Video Face Manipulation Detection through Ensemble of CNNs

Nicolo Bonettini, Edoardo Daniele Cannas, Sara Mandelli, Luca Bondi, Paolo Bestagini, Stefano Tubaro

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Auto-TLDR; Face Manipulation Detection in Video Sequences Using Convolutional Neural Networks

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In the last few years, several techniques for facial manipulation in videos have been successfully developed and made available to the masses (i.e., FaceSwap, deepfake, etc.). These methods enable anyone to easily edit faces in video sequences with incredibly realistic results and a very little effort. Despite the usefulness of these tools in many fields, if used maliciously, they can have a significantly bad impact on society (e.g., fake news spreading, cyber bullying through fake revenge porn). The ability of objectively detecting whether a face has been manipulated in a video sequence is then a task of utmost importance. In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques. In particular, we study the ensembling of different trained Convolutional Neural Network (CNN) models. In the proposed solution, different models are obtained starting from a base network (i.e., EfficientNetB4) making use of two different concepts: (i) attention layers; (ii) siamese training. We show that combining these networks leads to promising face manipulation detection results on two publicly available datasets with more than 119000 videos.

G-FAN: Graph-Based Feature Aggregation Network for Video Face Recognition

He Zhao, Yongjie Shi, Xin Tong, Jingsi Wen, Xianghua Ying, Jinshi Hongbin Zha

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Auto-TLDR; Graph-based Feature Aggregation Network for Video Face Recognition

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In this paper, we propose a graph-based feature aggregation network (G-FAN) for video face recognition. Compared with the still image, video face recognition exhibits great challenges due to huge intra-class variability and high inter-class ambiguity. To address this problem, our G-FAN first uses a Convolutional Neural Network to extract deep features for every input face of a subject. Then, we build an affinity graph based on the relation between facial features and apply Graph Convolutional Network to generate fine-grained quality vectors for each frame. Finally, the features among multiple frames are adaptively aggregated into a discriminative vector to represent a video face. Different from previous works that take a single image as input, our G-FAN could utilize the correlation information between image pairs and aggregate a template of faces simultaneously. The experiments on video face recognition benchmarks, including YTF, IJB-A, and IJB-C show that: (i) G-FAN automatically learns to advocate high-quality frames while repelling low-quality ones. (ii) G-FAN significantly boosts recognition accuracy and outperforms other state-of-the-art aggregation methods.

Local Attention and Global Representation Collaborating for Fine-Grained Classification

He Zhang, Yunming Bai, Hui Zhang, Jing Liu, Xingguang Li, Zhaofeng He

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Auto-TLDR; Weighted Region Network for Cosmetic Contact Lenses Detection

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The cosmetic contact lenses over an iris may change its original textural pattern that is the foundation for iris recognition, making the cosmetic lenses a possible and easy-to-use iris presentation attack means. Aiming at cosmetic contact lenses detection of practical application system, some approaches have been proposed but still facing unsolved problems, such as low quality iris images and inaccurate localized iris boundaries. In this paper, we propose a novel framework called Weighted Region Network (WRN) for the cosmetic contact lenses detection. The WRN includes both the local attention Weight Network and the global classification Region Network. With the inherent attention mechanism, the proposed network is able to find the most discriminative regions, which reduces the requirement for target detection and improves the ability of classification based on some specific areas and patterns. The Weight Network can be trained by using Rank loss and MSE loss without manual discriminative region annotations. Experiments are conducted on several databases and a new collected low-quality iris image database. The proposed method outperforms state-of-the-art fake iris detection algorithms, and is also effective for the fine-grained image classification task.

Exploring Seismocardiogram Biometrics with Wavelet Transform

Po-Ya Hsu, Po-Han Hsu, Hsin-Li Liu

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Auto-TLDR; Seismocardiogram Biometric Matching Using Wavelet Transform and Deep Learning Models

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Seismocardiogram (SCG) has become easily accessible in the past decade owing to the advance of sensor technology. However, SCG biometric has not been widely explored. In this paper, we propose combining wavelet transform together with deep learning models, machine learning classifiers, or structural similarity metric to perform SCG biometric matching tasks. We validate the proposed methods on the publicly available dataset from PhysioNet database. The dataset contains one hour long electrocardiogram, breathing, and SCG data of 20 subjects. We train the models on the first five minute SCG and conduct identification on the last five minute SCG. We evaluate the identification and authentication performance with recognition rate and equal error rate, respectively. Based on the results, we show that wavelet transformed SCG biometric can achieve state-of-the-art performance when combined with deep learning models, machine learning classifiers, or structural similarity.

Handwritten Signature and Text Based User Verification Using Smartwatch

Raghavendra Ramachandra, Sushma Venkatesh, Raja Kiran, Christoph Busch

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Auto-TLDR; A novel technique for user verification using a smartwatch based on writing pattern or signing pattern

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Wrist-wearable devices such as smartwatch have gained popularity as they provide quick access to the various information and easy access to multiple applications. Among various applications of the smartwatch, user verification based on the handwriting has been recently investigated. In this paper, we present a novel technique for user verification using a smartwatch based on writing pattern or signing pattern. The proposed technique leverages accelerometer data captured from the smartwatch that are further represented using 2D Continuous Wavelet Transform (CWT) and deep features extracted using the pre-trained ResNet50. The comparison is performed using the ensemble of the classifier. Extensive experiments are carried out on the newly captured dataset using two different smartwatches with three different writing scenarios (or activities). The article provides key insights and analysis of the results in such a verification scenario.