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Unsupervised Disentangling of Viewpoint and Residues Variations by Substituting Representations for Robust Face Recognition

Minsu Kim, Joanna Hong, Junho Kim, Hong Joo Lee, Yong Man Ro

Auto-TLDR; Unsupervised Disentangling of Identity, viewpoint, and Residue Representations for Robust Face Recognition

Abstract Slides Poster

It is well-known that identity-unrelated variations (e.g., viewpoint or illumination) degrade the performances of face recognition methods. In order to handle this challenge, a robust method for disentangling the identity and view representations has drawn an attention in the machine learning area. However, existing methods learn discriminative features which require a manual supervision of such factors of variations. In this paper, we propose a novel disentangling framework through modeling three representations of identity, viewpoint, and residues (i.e., identity and pose unrelated) which do not require supervision of the variations. By jointly modeling the three representations, we enhance the disentanglement of each representation and achieve robust face recognition performance. Further, the learned viewpoint representation can be utilized for pose estimation or editing of a posed facial image. Extensive quantitative and qualitative evaluations verify the effectiveness of our proposed method which disentangles identity, viewpoint, and residues of facial images.

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Contrastive Data Learning for Facial Pose and Illumination Normalization

Gee-Sern Hsu, Chia-Hao Tang

Auto-TLDR; Pose and Illumination Normalization with Contrast Data Learning for Face Recognition

Abstract Slides Poster Similar

Face normalization can be a crucial step when handling generic face recognition. We propose the Pose and Illumination Normalization (PIN) framework with contrast data learning for face normalization. The PIN framework is designed to learn the transformation from a source set to a target set. The source set and the target set compose a contrastive data set for learning. The source set contains faces collected in the wild and thus covers a wide range of variation across illumination, pose, expression and other variables. The target set contains face images taken under controlled conditions and all faces are in frontal pose and balanced in illumination. The PIN framework is composed of an encoder, a decoder and two discriminators. The encoder is made of a state-of-the-art face recognition network and acts as a facial feature extractor, which is not updated during training. The decoder is trained on both the source and target sets, and aims to learn the transformation from the source set to the target set; and therefore, it can transform an arbitrary face into a illumination and pose normalized face. The discriminators are trained to ensure the photo-realistic quality of the normalized face images generated by the decoder. The loss functions employed in the decoder and discriminators are appropriately designed and weighted for yielding better normalization outcomes and recognition performance. We verify the performance of the propose framework on several benchmark databases, and compare with state-of-the-art approaches.

Exemplar Guided Cross-Spectral Face Hallucination Via Mutual Information Disentanglement

Haoxue Wu, Huaibo Huang, Aijing Yu, Jie Cao, Zhen Lei, Ran He

Auto-TLDR; Exemplar Guided Cross-Spectral Face Hallucination with Structural Representation Learning

Abstract Slides Poster Similar

Recently, many Near infrared-visible (NIR-VIS) heterogeneous face recognition (HFR) methods have been proposed in the community. But it remains a challenging problem because of the sensing gap along with large pose variations. In this paper, we propose an Exemplar Guided Cross-Spectral Face Hallucination (EGCH) to reduce the domain discrepancy through disentangled representation learning. For each modality, EGCH contains a spectral encoder as well as a structure encoder to disentangle spectral and structure representation, respectively. It also contains a traditional generator that reconstructs the input from the above two representations, and a structure generator that predicts the facial parsing map from the structure representation. Besides, mutual information minimization and maximization are conducted to boost disentanglement and make representations adequately expressed. Then the translation is built on structure representations between two modalities. Provided with the transformed NIR structure representation and original VIS spectral representation, EGCH is capable to produce high-fidelity VIS images that preserve the topology structure of the input NIR while transfer the spectral information of an arbitrary VIS exemplar. Extensive experiments demonstrate that the proposed method achieves more promising results both qualitatively and quantitatively than the state-of-the-art NIR-VIS methods.

Learning Disentangled Representations for Identity Preserving Surveillance Face Camouflage

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

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

Abstract Poster Similar

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.

DAIL: Dataset-Aware and Invariant Learning for Face Recognition

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

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

Abstract Slides Poster Similar

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.

SATGAN: Augmenting Age Biased Dataset for Cross-Age Face Recognition

Wenshuang Liu, Wenting Chen, Yuanlue Zhu, Linlin Shen

Auto-TLDR; SATGAN: Stable Age Translation GAN for Cross-Age Face Recognition

Abstract Slides Poster Similar

In this paper, we propose a Stable Age Translation GAN (SATGAN) to generate fake face images at different ages to augment age biased face datasets for Cross-Age Face Recognition (CAFR) . The proposed SATGAN consists of both generator and discriminator. As a part of the generator, a novel Mask Attention Module (MAM) is introduced to make the generator focus on the face area. In addition, the generator employs a Uniform Distribution Discriminator (UDD) to supervise the learning of latent feature map and enforce the uniform distribution. Besides, the discriminator employs a Feature Separation Module (FSM) to disentangle identity information from the age information. The quantitative and qualitative evaluations on Morph dataset prove that SATGAN achieves much better performance than existing methods. The face recognition model trained using dataset (VGGFace2 and MS-Celeb-1M) augmented using our SATGAN achieves better accuracy on cross age dataset like Cross-Age LFW and AgeDB-30.

Pose-Robust Face Recognition by Deep Meta Capsule Network-Based Equivariant Embedding

Fangyu Wu, Jeremy Simon Smith, Wenjin Lu, Bailing Zhang

Auto-TLDR; Deep Meta Capsule Network-based Equivariant Embedding Model for Pose-Robust Face Recognition

Abstract Similar

Despite the exceptional success in face recognition related technologies, handling large pose variations still remains a key challenge. Current techniques for pose-robust face recognition either, directly extract pose-invariant features, or first synthesize a face that matches the target pose before feature extraction. It is more desirable to learn face representations equivariant to pose variations. To this end, this paper proposes a deep meta Capsule network-based Equivariant Embedding Model (DM-CEEM) with three distinct novelties. First, the proposed RB-CapsNet allows DM-CEEM to learn an equivariant embedding for pose variations and achieve the desired transformation for input face images. Second, we introduce a new version of a Capsule network called RB-CapsNet to extend CapsNet to perform a profile-to-frontal face transformation in deep feature space. Third, we train the DM-CEEM in a meta way by treating a single overall classification target as multiple sub-tasks that satisfy certain unknown probabilities. In each sub-task, we sample the support and query sets randomly. The experimental results on both controlled and in-the-wild databases demonstrate the superiority of DM-CEEM over state-of-the-art.

Facial Expression Recognition by Using a Disentangled Identity-Invariant Expression Representation

Kamran Ali, Charles Hughes

Auto-TLDR; Transfer-based Expression Recognition Generative Adversarial Network (TER-GAN)

Abstract Slides Poster Similar

Facial Expression Recognition (FER) is a challenging task because many factors of variation such as pose, illumination, and identity-specific attributes are entangled with the expression information in an expressive face image. Recent works show that the performance of a FER algorithm can be improved by disentangling the expression information from identity features. In this paper, we present Transfer-based Expression Recognition Generative Adversarial Network (TER-GAN) that combines the effectiveness of a novel feature disentanglement technique with the concept of identity-invariant expression representation learning for facial expression recognition. More specifically, TER-GAN learns a disentangled expression representation by extracting expression features from one image and transferring the expression information to the identity of another image. To improve the feature disentanglement process, and to learn an identity-invariant expression representation, we introduce a novel expression consistency loss and an identity consistency loss that exploit expression and identity information from both real and synthetic images. We evaluated the performance of our proposed facial expression recognition technique by employing five public facial expression databases, CK+, Oulu-CASIA, MMI, BU-3DFE, and BU-4DFE, the latter being used for pre-training. The experimental results show the effectiveness of the proposed technique.

Age Gap Reducer-GAN for Recognizing Age-Separated Faces

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

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

Abstract Slides Poster Similar

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.

DFH-GAN: A Deep Face Hashing with Generative Adversarial Network

Bo Xiao, Lanxiang Zhou, Yifei Wang, Qiangfang Xu

Auto-TLDR; Deep Face Hashing with GAN for Face Image Retrieval

Abstract Slides Poster Similar

Face Image retrieval is one of the key research directions in computer vision field. Thanks to the rapid development of deep neural network in recent years, deep hashing has achieved good performance in the field of image retrieval. But for large-scale face image retrieval, the performance needs to be further improved. In this paper, we propose Deep Face Hashing with GAN (DFH-GAN), a novel deep hashing method for face image retrieval, which mainly consists of three components: a generator network for generating synthesized images, a discriminator network with a shared CNN to learn multi-domain face feature, and a hash encoding network to generate compact binary hash codes. The generator network is used to perform data augmentation so that the model could learn from both real images and diverse synthesized images. We adopt a two-stage training strategy. In the first stage, the GAN is trained to generate fake images, while in the second stage, to make the network convergence faster. The model inherits the trained shared CNN of discriminator to train the DFH model by using many different supervised loss functions not only in the last layer but also in the middle layer of the network. Extensive experiments on two widely used datasets demonstrate that DFH-GAN can generate high-quality binary hash codes and exceed the performance of the state-of-the-art model greatly.

Unsupervised Face Manipulation Via Hallucination

Keerthy Kusumam, Enrique Sanchez, Georgios Tzimiropoulos

Auto-TLDR; Unpaired Face Image Manipulation using Autoencoders

Abstract Slides Poster Similar

This paper addresses the problem of manipulatinga face image in terms of changing its pose. To achieve this, wepropose a new method that can be trained under the very general“unpaired” setting. To this end, we firstly propose to modelthe general appearance, layout and background of the inputimage using a low-resolution version of it which is progressivelypassed through a hallucination network to generate featuresat higher resolutions. We show that such a formulation issignificantly simpler than previous approaches for appearancemodelling based on autoencoders. Secondly, we propose a fullylearnable and spatially-aware appearance transfer module whichcan cope with misalignment between the input source image andthe target pose and can effectively combine the features fromthe hallucination network with the features produced by ourgenerator. Thirdly, we introduce an identity preserving methodthat is trained in an unsupervised way, by using an auxiliaryfeature extractor and a contrastive loss between the real andgenerated images. We compare our method against the state-of-the-art reporting significant improvements both quantitatively, interms of FID and IS, and qualitatively.

Lightweight Low-Resolution Face Recognition for Surveillance Applications

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

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

Abstract Slides Poster Similar

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.

Quality-Based Representation for Unconstrained Face Recognition

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

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

Abstract Slides Similar

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.

Dual-MTGAN: Stochastic and Deterministic Motion Transfer for Image-To-Video Synthesis

Fu-En Yang, Jing-Cheng Chang, Yuan-Hao Lee, Yu-Chiang Frank Wang

Auto-TLDR; Dual Motion Transfer GAN for Convolutional Neural Networks

Abstract Slides Poster Similar

Generating videos with content and motion variations is a challenging task in computer vision. While the recent development of GAN allows video generation from latent representations, it is not easy to produce videos with particular content of motion patterns of interest. In this paper, we propose Dual Motion Transfer GAN (Dual-MTGAN), which takes image and video data as inputs while learning disentangled content and motion representations. Our Dual-MTGAN is able to perform deterministic motion transfer and stochastic motion generation. Based on a given image, the former preserves the input content and transfers motion patterns observed from another video sequence, and the latter directly produces videos with plausible yet diverse motion patterns based on the input image. The proposed model is trained in an end-to-end manner, without the need to utilize pre-defined motion features like pose or facial landmarks. Our quantitative and qualitative results would confirm the effectiveness and robustness of our model in addressing such conditioned image-to-video tasks.

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

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

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.

Angular Sparsemax for Face Recognition

Chi Ho Chan, Josef Kittler

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.

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

Debayan Deb, Divyansh Aggarwal, Anil Jain

Auto-TLDR; Aging Face Features for Missing Children Identification

Abstract Similar

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.

Face Image Quality Assessment for Model and Human Perception

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

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

Abstract Slides Poster Similar

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.

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

Samadhi Poornima Kumarasinghe Wickrama Arachchilage, Ebroul Izquierdo

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

Abstract Slides Poster Similar

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.

Cam-Softmax for Discriminative Deep Feature Learning

Tamas Suveges, Stephen James Mckenna

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

Abstract Slides Poster Similar

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.

Identity-Aware Facial Expression Recognition in Compressed Video

Xiaofeng Liu, Linghao Jin, Xu Han, Jun Lu, Jonghye Woo, Jane You

Auto-TLDR; Exploring Facial Expression Representation in Compressed Video with Mutual Information Minimization

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This paper targets to explore the inter-subject variations eliminated facial expression representation in the compressed video domain. Most of the previous methods process the RGB images of a sequence, while the off-the-shelf and valuable expression-related muscle movement already embedded in the compression format. In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possible to extract identity factors from the I frame with a pre-trained face recognition network. By enforcing the marginal independent of them, the expression feature is expected to be purer for the expression and be robust to identity shifts. Specifically, we propose a novel collaborative min-min game for mutual information (MI) minimization in latent space. We do not need the identity label or multiple expression samples from the same person for identity elimination. Moreover, when the apex frame is annotated in the dataset, the complementary constraint can be further added to regularize the feature-level game. In testing, only the compressed residual frames are required to achieve expression prediction. Our solution can achieve comparable or better performance than the recent decoded image based methods on the typical FER benchmarks with about 3$\times$ faster inference with compressed data.

Coherence and Identity Learning for Arbitrary-Length Face Video Generation

Shuquan Ye, Chu Han, Jiaying Lin, Guoqiang Han, Shengfeng He

Auto-TLDR; Face Video Synthesis Using Identity-Aware GAN and Face Coherence Network

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Face synthesis is an interesting yet challenging task in computer vision. It is even much harder to generate a portrait video than a single image. In this paper, we propose a novel video generation framework for synthesizing arbitrary-length face videos without any face exemplar or landmark. To overcome the synthesis ambiguity of face video, we propose a divide-and-conquer strategy to separately address the video face synthesis problem from two aspects, face identity synthesis and rearrangement. To this end, we design a cascaded network which contains three components, Identity-aware GAN (IA-GAN), Face Coherence Network, and Interpolation Network. IA-GAN is proposed to synthesize photorealistic faces with the same identity from a set of noises. Face Coherence Network is designed to re-arrange the faces generated by IA-GAN while keeping the inter-frame coherence. Interpolation Network is introduced to eliminate the discontinuity between two adjacent frames and improve the smoothness of the face video. Experimental results demonstrate that our proposed network is able to generate face video with high visual quality while preserving the identity. Statistics show that our method outperforms state-of-the-art unconditional face video generative models in multiple challenging datasets.

Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and Visual Geometry

Oussema Bouafif, Bogdan Khomutenko, Mohammed Daoudi

Auto-TLDR; Recovering 3D Head Geometry from a Single Image using Deep Learning and Geometric Techniques

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Recovering the 3D geometric structure of a face from a single input image is a challenging active research area in computer vision. In this paper, we present a novel method for reconstructing 3D heads from a single or multiple image(s) using a hybrid approach based on deep learning and geometric techniques. We propose an encoder-decoder network based on the U-net architecture and trained on synthetic data only. It predicts both pixel-wise normal vectors and landmarks maps from a single input photo. Landmarks are used for the pose computation and the initialization of the optimization problem, which, in turn, reconstructs the 3D head geometry by using a parametric morphable model and normal vector fields. State-of-the-art results are achieved through qualitative and quantitative evaluation tests on both single and multi-view settings. Despite the fact that the model was trained only on synthetic data, it successfully recovers 3D geometry and precise poses for real-world images.

High Resolution Face Age Editing

Xu Yao, Gilles Puy, Alasdair Newson, Yann Gousseau, Pierre Hellier

Auto-TLDR; An Encoder-Decoder Architecture for Face Age editing on High Resolution Images

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Face age editing has become a crucial task in film post-production, and is also becoming popular for general purpose photography. Recently, adversarial training has produced some of the most visually impressive results for image manipulation, including the face aging/de-aging task. In spite of considerable progress, current methods often present visual artifacts and can only deal with low-resolution images. In order to achieve aging/de-aging with the high quality and robustness necessary for wider use, these problems need to be addressed. This is the goal of the present work. We present an encoder-decoder architecture for face age editing. The core idea of our network is to encode a face image to age-invariant features, and learn a modulation vector corresponding to a target age. We then combine these two elements to produce a realistic image of the person with the desired target age. Our architecture is greatly simplified with respect to other approaches, and allows for fine-grained age editing on high resolution images in a single unified model. Source codes are available at https://github.com/InterDigitalInc/HRFAE.

Pose Variation Adaptation for Person Re-Identification

Lei Zhang, Na Jiang, Qishuai Diao, Yue Xu, Zhong Zhou, Wei Wu

Auto-TLDR; Pose Transfer Generative Adversarial Network for Person Re-identification

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Person re-identification (reid) plays an important role in surveillance video analysis, especially for criminal investigation and intelligent security. Although a large number of effective feature or distance metric learning approaches have been proposed, it still suffers from pedestrians appearance variations caused by pose changing. Most of the previous methods address this problem by learning a pose-invariant descriptor subspace. In this paper, we propose a pose variation adaptation method for person reid in the view of data augmentation. It can reduce the probability of deep learning network over-fitting. Specifically, we introduce a pose transfer generative adversarial network with a similarity measurement constraint. With the learned pose transfer model, training images can be pose-transferred to any given poses, and along with the original images, form a augmented training dataset. It increases data diversity against over-fitting. In contrast to previous GAN-based methods, we consider the influence of pose variations on similarity measure to generate more realistic and shaper samples for person reid. Besides, we optimize hard example mining to introduce a novel manner of samples (pose-transferred images) used with the learned pose transfer model. It focuses on the inferior samples which are caused by pose variations to increase the number of effective hard examples for learning discriminative features and improve the generalization ability. We extensively conduct comparative evaluations to demonstrate the advantages and superiority of our proposed method over the state-of-the-art approaches on Market-1501 and DukeMTMC-reID, the rank-1 accuracy is 96.1% for Market-1501 and 92.0% for DukeMTMC-reID.

A Quantitative Evaluation Framework of Video De-Identification Methods

Sathya Bursic, Alessandro D'Amelio, Marco Granato, Giuliano Grossi, Raffaella Lanzarotti

Auto-TLDR; Face de-identification using photo-reality and facial expressions

Abstract Slides Poster Similar

We live in an era of privacy concerns, motivating a large research effort in face de-identification. As in other fields, we are observing a general movement from hand-crafted methods to deep learning methods, mainly involving generative models. Although these methods produce more natural de-identified images or videos, we claim that the mere evaluation of the de-identification is not sufficient, especially when it comes to processing the images/videos further. In this note, we take into account the issue of preserving privacy, facial expressions, and photo-reality simultaneously, proposing a general testing framework. The method is applied to four open-source tools, producing a baseline for future de-identification methods.

SSDL: Self-Supervised Domain Learning for Improved Face Recognition

Samadhi Poornima Kumarasinghe Wickrama Arachchilage, Ebroul Izquierdo

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.

Unsupervised Contrastive Photo-To-Caricature Translation Based on Auto-Distortion

Yuhe Ding, Xin Ma, Mandi Luo, Aihua Zheng, Ran He

Auto-TLDR; Unsupervised contrastive photo-to-caricature translation with style loss

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Photo-to-caricature aims to synthesize the caricature as a rendered image exaggerating the features through sketching, pencil strokes, or other artistic drawings. Style rendering and geometry deformation are the most important aspects in photo-to-caricature translation task. To take both into consideration, we propose an unsupervised contrastive photo-to-caricature translation architecture. Considering the intuitive artifacts in the existing methods, we propose a contrastive style loss for style rendering to enforce the similarity between the style of rendered photo and the caricature, and simultaneously enhance its discrepancy to the photos. To obtain an exaggerating deformation in an unpaired/unsupervised fashion, we propose a Distortion Prediction Module (DPM) to predict a set of displacements vectors for each input image while fixing some controlling points, followed by the thin plate spline interpolation for warping. The model is trained on unpaired photo and caricature while can offer bidirectional synthesizing via inputting either a photo or a caricature. Extensive experiments demonstrate that the proposed model is effective to generate hand-drawn like caricatures compared with existing competitors.

Learning Semantic Representations Via Joint 3D Face Reconstruction and Facial Attribute Estimation

Zichun Weng, Youjun Xiang, Xianfeng Li, Juntao Liang, Wanliang Huo, Yuli Fu

Auto-TLDR; Joint Framework for 3D Face Reconstruction with Facial Attribute Estimation

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We propose a novel joint framework for 3D face reconstruction (3DFR) that integrates facial attribute estimation (FAE) as an auxiliary task. One of the essential problems of 3DFR is to extract semantic facial features (e.g., Big Nose, High Cheekbones, and Asian) from in-the-wild 2D images, which is inherently involved with FAE. These two tasks, though heterogeneous, are highly relevant to each other. To achieve this, we leverage a Convolutional Neural Network to extract shared facial representations for both shape decoder and attribute classifier. We further develop an in-batch hybrid-task training scheme that enables our model to learn from heterogeneous facial datasets jointly within a mini-batch. Thanks to the joint loss that provides supervision from both 3DFR and FAE domains, our model learns the correlations between 3D shapes and facial attributes, which benefit both feature extraction and shape inference. Quantitative evaluation and qualitative visualization results confirm the effectiveness and robustness of our joint framework.

Disentangled Representation Based Face Anti-Spoofing

Zhao Liu, Zunlei Feng, Yong Li, Zeyu Zou, Rong Zhang, Mingli Song, Jianping Shen

Auto-TLDR; Face Anti-Spoofing using Motion Information and Disentangled Frame Work

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Face anti-spoofing is an important problem for both academic research and industrial face recognition systems. Most of the existing face anti-spoofing methods take it as a classification task on individual static images, where motion pattern differences in consecutive real or fake face sequences are ignored. In this work, we propose a novel method to identify spoofing patterns using motion information. Different from previous methods, the proposed method makes the real or fake decision on the disentangled feature level, based on the observation that motion and spoofing pattern features could be disentangled from original image frames. We design a representation disentangling frame- work for this task, which is able to reconstruct both real and fake face sequences from the input. Meanwhile, the disentangled representations could be used to classify whether the input faces are real or fake. We perform several experiments on Casia-FASD and ReplayAttack datasets. The proposed method achieves SOTA results compared with existing face anti-spoofing methods.

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

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

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.

Semantics-Guided Representation Learning with Applications to Visual Synthesis

Jia-Wei Yan, Ci-Siang Lin, Fu-En Yang, Yu-Jhe Li, Yu-Chiang Frank Wang

Auto-TLDR; Learning Interpretable and Interpolatable Latent Representations for Visual Synthesis

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Learning interpretable and interpolatable latent representations has been an emerging research direction, allowing researchers to understand and utilize the derived latent space for further applications such as visual synthesis or recognition. While most existing approaches derive an interpolatable latent space and induces smooth transition in image appearance, it is still not clear how to observe desirable representations which would contain semantic information of interest. In this paper, we aim to learn meaningful representations and simultaneously perform semantic-oriented and visually-smooth interpolation. To this end, we propose an angular triplet-neighbor loss (ATNL) that enables learning a latent representation whose distribution matches the semantic information of interest. With the latent space guided by ATNL, we further utilize spherical semantic interpolation for generating semantic warping of images, allowing synthesis of desirable visual data. Experiments on MNIST and CMU Multi-PIE datasets qualitatively and quantitatively verify the effectiveness of our method.

Controllable Face Aging

Haien Zeng, Hanjiang Lai

Auto-TLDR; A controllable face aging method via attribute disentanglement generative adversarial network

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Motivated by the following two observations: 1) people are aging differently under different conditions for changeable facial attributes, e.g., skin color may become darker when working outside, and 2) it needs to keep some unchanged facial attributes during the aging process, e.g., race and gender, we propose a controllable face aging method via attribute disentanglement generative adversarial network. To offer fine control over the synthesized face images, first, an individual embedding of the face is directly learned from an image that contains the desired facial attribute. Second, since the image may contain other unwanted attributes, an attribute disentanglement network is used to separate the individual embedding and learn the common embedding that contains information about the face attribute (e.g., race). With the common embedding, we can manipulate the generated face image with the desired attribute in an explicit manner. Experimental results on two common benchmarks demonstrate that our proposed generator achieves comparable performance on the aging effect with state-of-the-art baselines while gaining more flexibility for attribute control. Code is available at supplementary material.

Deep Multi-Task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature Sharing

Rui Zhao, Tianshan Liu, Jun Xiao, P. K. Daniel Lun, Kin-Man Lam

Auto-TLDR; Multi-task Learning for Facial Expression Recognition and Synthesis

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Multi-task learning is an effective learning strategy for deep-learning-based facial expression recognition tasks. However, most existing methods take into limited consideration the feature selection, when transferring information between different tasks, which may lead to task interference when training the multi-task networks. To address this problem, we propose a novel selective feature-sharing method, and establish a multi-task network for facial expression recognition and facial expression synthesis. The proposed method can effectively transfer beneficial features between different tasks, while filtering out useless and harmful information. Moreover, we employ the facial expression synthesis task to enlarge and balance the training dataset to further enhance the generalization ability of the proposed method. Experimental results show that the proposed method achieves state-of-the-art performance on those commonly used facial expression recognition benchmarks, which makes it a potential solution to real-world facial expression recognition problems.

Talking Face Generation Via Learning Semantic and Temporal Synchronous Landmarks

Aihua Zheng, Feixia Zhu, Hao Zhu, Mandi Luo, Ran He

Auto-TLDR; A semantic and temporal synchronous landmark learning method for talking face generation

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Abstract—Given a speech clip and facial image, the goal of talking face generation is to synthesize a talking face video with accurate mouth synchronization and natural face motion. Recent progress has proven the effectiveness of the landmarks as the intermediate information during talking face generation. However,the large gap between audio and visual modalities makes the prediction of landmarks challenging and limits generation ability. This paper proposes a semantic and temporal synchronous landmark learning method for talking face generation. First, we propose to introduce a word detector to enforce richer semantic information. Then, we propose to preserve the temporal synchronization and consistency between landmarks and audio via the proposed temporal residual loss. Lastly, we employ a U-Net generation network with adaptive reconstruction loss to generate facial images for the predicted landmarks. Experimental results on two benchmark datasets LRW and GRID demonstrate the effectiveness of our model compared to the state-of-the-art methods of talking face generation.

GarmentGAN: Photo-Realistic Adversarial Fashion Transfer

Amir Hossein Raffiee, Michael Sollami

Auto-TLDR; GarmentGAN: A Generative Adversarial Network for Image-Based Garment Transfer

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The garment transfer problem comprises two tasks: learning to separate a person's body (pose, shape, color) from their clothing (garment type, shape, style) and then generating new images of the wearer dressed in arbitrary garments. We present GarmentGAN, a new algorithm that performs image-based garment transfer through generative adversarial methods. The GarmentGAN framework allows users to virtually try-on items before purchase and generalizes to various apparel types. GarmentGAN requires as input only two images, namely, a picture of the target fashion item and an image containing the customer. The output is a synthetic image wherein the customer is wearing the target apparel. In order to make the generated image look photo-realistic, we employ the use of novel generative adversarial techniques. GarmentGAN improves on existing methods in the realism of generated imagery and solves various problems related to self-occlusions. Our proposed model incorporates additional information during training, utilizing both segmentation maps and body key-point information. We show qualitative and quantitative comparisons to several other networks to demonstrate the effectiveness of this technique.

Learning Emotional Blinded Face Representations

Alejandro Peña Almansa, Julian Fierrez, Agata Lapedriza, Aythami Morales

Auto-TLDR; Blind Face Representations for Emotion Recognition

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This work proposes two new face representations that are blind to the expressions associated to emotional responses. This work is in part motivated by new international regulations for personal data protection, which force data controllers to protect any kind of sensitive information involved in automatic processes. The advances in affective computing have contributed to improve human-machine interfaces, but at the same time, the capacity to monitorize emotional responses trigger potential risks for humans, both in terms of fairness and privacy. We propose two different methods to learn these facial expression blinded features. We show that it is possible to eliminate information related to emotion recognition tasks, while the performance of subject verification, gender recognition, and ethnicity classification are just slightly affected. We also present an application to train fairer classifiers over a protected facial expression attribute. The results demonstrate that it is possible to reduce emotional information in the face representation while retaining competitive performance in other face-based artificial intelligence tasks.

Person Recognition with HGR Maximal Correlation on Multimodal Data

Yihua Liang, Fei Ma, Yang Li, Shao-Lun Huang

Auto-TLDR; A correlation-based multimodal person recognition framework that learns discriminative embeddings of persons by joint learning visual features and audio features

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Multimodal person recognition is a common task in video analysis and public surveillance, where information from multiple modalities, such as images and audio extracted from videos, are used to jointly determine the identity of a person. Previous person recognition techniques either use only uni-modal data or only consider shared representations between different input modalities, while leaving the extraction of their relationship with identity information to downstream tasks. Furthermore, real-world data often contain noise, which makes recognition more challenging practical situations. In our work, we propose a novel correlation-based multimodal person recognition framework that is relatively simple but can efficaciously learn supervised information in multimodal data fusion and resist noise. Specifically, our framework learns a discriminative embeddings of persons by joint learning visual features and audio features while maximizing HGR maximal correlation among multimodal input and persons' identities. Experiments are done on a subset of Voxceleb2. Compared with state-of-the-art methods, the proposed method demonstrates an improvement of accuracy and robustness to noise.

Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric Learning

Vladislav Sovrasov, Dmitry Sidnev

Auto-TLDR; Cross-Domain Generalization in Person Re-identification using Omni-Scale Network

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This work considers the problem of domain shift in person re-identification.Being trained on one dataset, a re-identification model usually performs much worse on unseen data. Partially this gap is caused by the relatively small scale of person re-identification datasets (compared to face recognition ones, for instance), but it is also related to training objectives. We propose to use the metric learning objective, namely AM-Softmax loss, and some additional training practices to build well-generalizing, yet, computationally efficient models. We use recently proposed Omni-Scale Network (OSNet) architecture combined with several training tricks and architecture adjustments to obtain state-of-the art results in cross-domain generalization problem on a large-scale MSMT17 dataset in three setups: MSMT17-all->DukeMTMC, MSMT17-train->Market1501 and MSMT17-all->Market1501.

Attentive Hybrid Feature Based a Two-Step Fusion for Facial Expression Recognition

Jun Weng, Yang Yang, Zichang Tan, Zhen Lei

Auto-TLDR; Attentive Hybrid Architecture for Facial Expression Recognition

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Facial expression recognition is inherently a challenging task, especially for the in-the-wild images with various occlusions and large pose variations, which may lead to the loss of some crucial information. To address it, in this paper, we propose an attentive hybrid architecture (AHA) which learns global, local and integrated features based on different face regions. Compared with one type of feature, our extracted features own complementary information and can reduce the loss of crucial information. Specifically, AHA contains three branches, where all sub-networks in those branches employ the attention mechanism to further localize the interested pixels/regions. Moreover, we propose a two-step fusion strategy based on LSTM to deeply explore the hidden correlations among different face regions. Extensive experiments on four popular expression databases (i.e., CK+, FER-2013, SFEW 2.0, RAF-DB) show the effectiveness of the proposed method.

SoftmaxOut Transformation-Permutation Network for Facial Template Protection

Hakyoung Lee, Cheng Yaw Low, Andrew Teoh

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.

Fixed Simplex Coordinates for Angular Margin Loss in CapsNet

Rita Pucci, Christian Micheloni, Gian Luca Foresti, Niki Martinel

Auto-TLDR; angular margin loss for capsule networks

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A more stationary and discriminative embedding is necessary for robust classification of images. We focus our attention on the newel CapsNet model and we propose the angular margin loss function in composition with margin loss. We define a fixed classifier implemented with fixed weights vectors obtained by the vertex coordinates of a simplex polytope. The advantage of using simplex polytope is that we obtain the maximal symmetry for stationary features angularly centred. Each weight vector is to be considered as the centroid of a class in the dataset. The embedding of an image is obtained through the capsule network encoding phase, that is identified as digitcaps matrix. Based on the centroids from the simplex coordinates and the embedding from the model, we compute the angular distance between the image embedding and the centroid of the correspondent class of the image. We take this angular distance as angular margin loss. We keep the computation proposed for margin loss in the original architecture of CapsNet. We train the model to minimise the angular between the embedding and the centroid of the class and maximise the magnitude of the embedding for the predicted class. The experiments on different datasets demonstrate that the angular margin loss improves the capability of capsule networks with complex datasets.

Image Representation Learning by Transformation Regression

Xifeng Guo, Jiyuan Liu, Sihang Zhou, En Zhu, Shihao Dong

Auto-TLDR; Self-supervised Image Representation Learning using Continuous Parameter Prediction

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Self-supervised learning is a thriving research direction since it can relieve the burden of human labeling for machine learning by seeking for supervision from data instead of human annotation. Although demonstrating promising performance in various applications, we observe that the existing methods usually model the auxiliary learning tasks as classification tasks with finite discrete labels, leading to insufficient supervisory signals, which in turn restricts the representation quality. In this paper, to solve the above problem and make full use of the supervision from data, we design a regression model to predict the continuous parameters of a group of transformations, i.e., image rotation, translation, and scaling. Surprisingly, this naive modification stimulates tremendous potential from data and the resulting supervisory signal has largely improved the performance of image representation learning. Extensive experiments on four image datasets, including CIFAR10, CIFAR100, STL10, and SVHN, indicate that our proposed algorithm outperforms the state-of-the-art unsupervised learning methods by a large margin in terms of classification accuracy. Crucially, we find that with our proposed training mechanism as an initialization, the performance of the existing state-of-the-art classification deep architectures can be preferably improved.

Lookalike Disambiguation: Improving Face Identification Performance at Top Ranks

Thomas Swearingen, Arun Ross

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.

Unsupervised Learning of Landmarks Based on Inter-Intra Subject Consistencies

Weijian Li, Haofu Liao, Shun Miao, Le Lu, Jiebo Luo

Auto-TLDR; Unsupervised Learning for Facial Landmark Discovery using Inter-subject Landmark consistencies

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We present a novel unsupervised learning approach to image landmark discovery by incorporating the inter-subject landmark consistencies on facial images. This is achieved via an inter-subject mapping module that transforms original subject landmarks based on an auxiliary subject-related structure. To recover from the transformed images back to the original subject, the landmark detector is forced to learn spatial locations that contain the consistent semantic meanings both for the paired intra-subject images and between the paired inter-subject images. Our proposed method is extensively evaluated on two public facial image datasets (MAFL, AFLW) with various settings. Experimental results indicate that our method can extract the consistent landmarks for both datasets and achieve better performances compared to the previous state-of-the-art methods quantitatively and qualitatively.

Group-Wise Feature Orthogonalization and Suppression for GAN Based Facial Attribute Translation

Zhiwei Wen, Haoqian Wu, Weicheng Xie, Linlin Shen

Auto-TLDR; Semantic Disentanglement of Generative Adversarial Network

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Generative Adversarial Network (GAN) has been widely used for object attribute editing. However, the semantic correlation, resulted from the feature map interaction in the generative network of GAN, may impair the generalization ability of the generative network. In this work, semantic disentanglement is introduced in GAN to reduce the attribute correlation. The feature maps of the generative network are first grouped with an efficient clustering algorithm based on hash encoding, which are used to excavate hidden semantic attributes and calculate the group-wise orthogonality loss for the reduction of attribute entanglement. Meanwhile, the feature maps falling in the intersection regions of different groups are further suppressed to reduce the attribute-wise interaction. Extensive experiments reveal that the proposed GAN generated more genuine objects than the state of the arts. Quantitative results of classification accuracy, inception and FID scores further justify the effectiveness of the proposed GAN.

RGB-Infrared Person Re-Identification Via Image Modality Conversion

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

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.

Identity-Preserved Face Beauty Transformation with Conditional Generative Adversarial Networks

Zhitong Huang, Ching Y Suen

Auto-TLDR; Identity-preserved face beauty transformation using conditional GANs

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Identity-preserved face beauty transformation aims to change the beauty scale of a face image while preserving the identity of the original face. In our framework of conditional Generative Adversarial Networks (cGANs), the synthesized face produced by the generator would have the same beauty scale indicated by the input condition. Unlike the discrete class labels used in most cGANs, the condition of target beauty scale in our framework is given by a continuous real-valued beauty score in the range [1 to 5], which makes the work challenging. To tackle the problem, we have implemented a triple structure, in which the conditional discriminator is divided into a normal discriminator and a separate face beauty predictor. We have also developed another new structure called Conditioned Instance Normalization to replace the original concatenation used in cGANs, which makes the combination of the input image and condition more effective. Furthermore, Self-Consistency Loss is introduced as a new parameter to improve the stability of training and quality of the generated image. In the end, the objectives of beauty transformation and identity preservation are evaluated by the pretrained face beauty predictor and state-of-the-art face recognition network. The result is encouraging and it also shows that certain facial features could be synthesized by the generator according to the target beauty scale, while preserving the original identity.

Domain Generalized Person Re-Identification Via Cross-Domain Episodic Learning

Ci-Siang Lin, Yuan Chia Cheng, Yu-Chiang Frank Wang

Auto-TLDR; Domain-Invariant Person Re-identification with Episodic Learning

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Aiming at recognizing images of the same person across distinct camera views, person re-identification (re-ID) has been among active research topics in computer vision. Most existing re-ID works require collection of a large amount of labeled image data from the scenes of interest. When the data to be recognized are different from the source-domain training ones, a number of domain adaptation approaches have been proposed. Nevertheless, one still needs to collect labeled or unlabelled target-domain data during training. In this paper, we tackle an even more challenging and practical setting, domain generalized (DG) person re-ID. That is, while a number of labeled source-domain datasets are available, we do not have access to any target-domain training data. In order to learn domain-invariant features without knowing the target domain of interest, we present an episodic learning scheme which advances meta learning strategies to exploit the observed source-domain labeled data. The learned features would exhibit sufficient domain-invariant properties while not overfitting the source-domain data or ID labels. Our experiments on four benchmark datasets confirm the superiority of our method over the state-of-the-arts.