Inner Eye Canthus Localization for Human Body Temperature Screening

Claudio Ferrari, Lorenzo Berlincioni, Marco Bertini, Alberto Del Bimbo

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Auto-TLDR; Automatic Localization of the Inner Eye Canthus in Thermal Face Images using 3D Morphable Face Model

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In this paper, we propose an automatic approach for localizing the inner eye canthus in thermal face images. We first coarsely detect 5 facial keypoints corresponding to the center of the eyes, the nosetip and the ears. Then we compute a sparse 2D-3D points correspondence using a 3D Morphable Face Model (3DMM). This correspondence is used to project the entire 3D face onto the image, and subsequently locate the inner eye canthus. Detecting this location allows to obtain the most precise body temperature measurement for a person using a thermal camera. We evaluated the approach on a thermal face dataset provided with manually annotated landmarks. However, such manual annotations are normally conceived to identify facial parts such as eyes, nose and mouth, and are not specifically tailored for localizing the eye canthus region. As additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images. Then, by manually selecting a small region corresponding to the eye canthus, we enrich the dataset with additional annotations. By using the manual landmarks, we ensure the correctness of the 3DMM projection, which can be used as ground-truth for future evaluations. Moreover, we supply the dataset with the 3D head poses and per-point visibility masks for detecting self-occlusions. The data will be publicly released.

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

Oussema Bouafif, Bogdan Khomutenko, Mohammed Daoudi

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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.

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.

Sequential Non-Rigid Factorisation for Head Pose Estimation

Stefania Cristina, Kenneth Patrick Camilleri

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Auto-TLDR; Sequential Shape-and-Motion Factorisation for Head Pose Estimation in Eye-Gaze Tracking

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Within the context of eye-gaze tracking, the capability of permitting the user to move naturally is an important step towards allowing for more natural user interaction in less constrained scenarios. Natural movement can be characterised by changes in head pose, as well as non-rigid face deformations as the user performs different facial expressions. While the estimation of head pose within the domain of eye-gaze tracking is being increasingly considered, the face is most often regarded as a rigid body. The few methods that factor the challenge of handling face deformations into the head pose estimation problem, often require the availability of a pre-defined face model or a considerable amount of training data. In this paper, we direct our attention towards the application of shape-and-motion factorisation for head pose estimation, since this does not generally rely on the availability of an initial face model. Over the years, various shape-and-motion factorisation methods have been proposed to address the challenges of rigid and non-rigid shape and motion recovery, in a batch or sequential manner. However, the real-time recovery of non-rigid shape and motion by factorisation remains, in general, an open problem. Our work addresses this open problem by proposing a sequential factorisation method for non-rigid shape and motion recovery, which does not rely on the availability of a pre-defined face deformation model or training data. Quantitative and qualitative results show that our method can handle various non-rigid face deformations without deterioration of the head pose estimation accuracy.

HP2IFS: Head Pose Estimation Exploiting Partitioned Iterated Function Systems

Carmen Bisogni, Michele Nappi, Chiara Pero, Stefano Ricciardi

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Auto-TLDR; PIFS based head pose estimation using fractal coding theory and Partitioned Iterated Function Systems

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Estimating the actual head orientation from 2D images, with regard to its three degrees of freedom, is a well known problem that is highly significant for a large number of applications involving head pose knowledge. Consequently, this topic has been tackled by a plethora of methods and algorithms the most part of which exploits neural networks. Machine learning methods, indeed, achieve accurate head rotation values yet require an adequate training stage and, to that aim, a relevant number of positive and negative examples. In this paper we take a different approach to this topic by using fractal coding theory and particularly Partitioned Iterated Function Systems to extract the fractal code from the input head image and to compare this representation to the fractal code of a reference model through Hamming distance. According to experiments conducted on both the BIWI and the AFLW2000 databases, the proposed PIFS based head pose estimation method provides accurate yaw/pitch/roll angular values, with a performance approaching that of state of the art of machine-learning based algorithms and exceeding most of non-training based approaches.

Multi-Attribute Regression Network for Face Reconstruction

Xiangzheng Li, Suping Wu

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Auto-TLDR; A Multi-Attribute Regression Network for Face Reconstruction

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In this paper, we propose a multi-attribute regression network (MARN) to investigate the problem of face reconstruction, especially in challenging cases when faces undergo large variations including severe poses, extreme expressions, and partial occlusions in unconstrained environments. The traditional 3DMM parametric regression method is absent from the learning of identity, expression, and attitude attributes, resulting in lacking geometric details in the reconstructed face. Our MARN method is to enable the network to better extract the feature information of face identity, expression, and pose attributes. We introduced identity, expression, and pose attribute loss functions to enhance the learning of details in each attribute. At the same time, we carefully design the geometric contour constraint loss function and use the constraints of sparse 2D face landmarks to improve the reconstructed geometric contour information. The experimental results show that our face reconstruction method has achieved significant results on the AFLW2000-3D and AFLW datasets compared with the most advanced methods. In addition, there has been a great improvement in dense face alignment. .

Interpretable Emotion Classification Using Temporal Convolutional Models

Manasi Bharat Gund, Abhiram Ravi Bharadwaj, Ifeoma Nwogu

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Auto-TLDR; Understanding the Dynamics of Facial Emotion Expression with Spatiotemporal Representations

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As with many problems solved by deep neural networks, existing solutions rarely explain, precisely, the important factors responsible for the predictions made by the model. This work looks to investigate how different spatial regions and landmark points change in position over time, to better explain the underlying factors responsible for various facial emotion expressions. By pinpointing the specific regions or points responsible for the classification of a particular facial expression, we gain better insight into the dynamics of the face when displaying that emotion. To accomplish this, we examine two spatiotemporal representations of moving faces, while expressing different emotions. The representations are then presented to a convolutional neural network for emotion classification. Class activation maps are used in highlighting the regions of interest and the results are qualitatively compared with the well known facial action units, using the facial action coding system. The model was originally trained and tested on the CK+ dataset for emotion classification, and then generalized to the SAMM dataset. In so doing, we successfully present an interpretable technique for understanding the dynamics that occur during convolutional-based prediction tasks on sequences of face data.

RefiNet: 3D Human Pose Refinement with Depth Maps

Andrea D'Eusanio, Stefano Pini, Guido Borghi, Roberto Vezzani, Rita Cucchiara

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Auto-TLDR; RefiNet: A Multi-stage Framework for 3D Human Pose Estimation

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Human Pose Estimation is a fundamental task for many applications in the Computer Vision community and it has been widely investigated in the 2D domain, i.e. intensity images. Therefore, most of the available methods for this task are mainly based on 2D Convolutional Neural Networks and huge manually-annotated RGB datasets, achieving stunning results. In this paper, we propose RefiNet, a multi-stage framework that regresses an extremely-precise 3D human pose estimation from a given 2D pose and a depth map. The framework consists of three different modules, each one specialized in a particular refinement and data representation, i.e. depth patches, 3D skeleton and point clouds. Moreover, we collect a new dataset, namely Baracca, acquired with RGB, depth and thermal cameras and specifically created for the automotive context. Experimental results confirm the quality of the refinement procedure that largely improves the human pose estimations of off-the-shelf 2D methods.

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

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

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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.

Attribute-Based Quality Assessment for Demographic Estimation in Face Videos

Fabiola Becerra-Riera, Annette Morales-González, Heydi Mendez-Vazquez, Jean-Luc Dugelay

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Auto-TLDR; Facial Demographic Estimation in Video Scenarios Using Quality Assessment

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Most existing works regarding facial demographic estimation are focused on still image datasets, although nowadays the need to analyze video content in real applications is increasing. We propose to tackle gender, age and ethnicity estimation in the context of video scenarios. Our main contribution is to use an attribute-specific quality assessment procedure to select best quality frames from a video sequence for each of the three demographic modalities. Best quality frames are classified with fine-tuned MobileNet models and a final video prediction is obtained with a majority voting strategy among the best selected frames. Our validation on three different datasets and our comparison with state-of-the-art models, show the effectiveness of the proposed demographic classifiers and the quality pipeline, which allows to reduce both: the number of frames to be classified and the processing time in practical applications; and improves the soft biometrics prediction accuracy.

Exploring Severe Occlusion: Multi-Person 3D Pose Estimation with Gated Convolution

Renshu Gu, Gaoang Wang, Jenq-Neng Hwang

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Auto-TLDR; 3D Human Pose Estimation for Multi-Human Videos with Occlusion

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3D human pose estimation (HPE) is crucial in human behavior analysis, augmented reality/virtual reality (AR/VR) applications, and self-driving industry. Videos that contain multiple potentially occluded people captured from freely moving monocular cameras are very common in real-world scenarios, while 3D HPE for such scenarios is quite challenging, partially because there is a lack of such data with accurate 3D ground truth labels in existing datasets. In this paper, we propose a temporal regression network with a gated convolution module to transform 2D joints to 3D and recover the missing occluded joints in the meantime. A simple yet effective localization approach is further conducted to transform the normalized pose to the global trajectory. To verify the effectiveness of our approach, we also collect a new moving camera multi-human (MMHuman) dataset that includes multiple people with heavy occlusion captured by moving cameras. The 3D ground truth joints are provided by accurate motion capture (MoCap) system. From the experiments on static-camera based Human3.6M data and our own collected moving-camera based data, we show that our proposed method outperforms most state-of-the-art 2D-to-3D pose estimation methods, especially for the scenarios with heavy occlusions.

Object Features and Face Detection Performance: Analyses with 3D-Rendered Synthetic Data

Jian Han, Sezer Karaoglu, Hoang-An Le, Theo Gevers

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Auto-TLDR; Synthetic Data for Face Detection Using 3DU Face Dataset

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This paper is to provide an overview of how object features from images influence face detection performance, and how to select synthetic faces to address specific features. To this end, we investigate the effects of occlusion, scale, viewpoint, background, and noise by using a novel synthetic image generator based on 3DU Face Dataset. To examine the effects of different features, we selected three detectors (Faster RCNN, HR, SSH) as representative of various face detection methodologies. Comparing different configurations of synthetic data on face detection systems, it showed that our synthetic dataset could complement face detectors to become more robust against features in the real world. Our analysis also demonstrated that a variety of data augmentation is necessary to address nuanced differences in performance.

Real-Time Driver Drowsiness Detection Using Facial Action Units

Malaika Vijay, Nandagopal Netrakanti Vinayak, Maanvi Nunna, Subramanyam Natarajan

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Auto-TLDR; Real-Time Detection of Driver Drowsiness using Facial Action Units using Extreme Gradient Boosting

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This paper presents a two-stage, vision-based pipeline for the real-time detection of driver drowsiness using Facial Action Units (FAUs). FAUs capture movements in groups of muscles in the face like widening of the eyes or dropping of the jaw. The first stage of the pipeline employs a Convolutional Neural Network (CNN) trained to detect FAUs. The output of the penultimate layer of this network serves as an image embedding that captures features relevant to FAU detection. These embeddings are then used to predict drowsiness using an Extreme Gradient Boosting (XGBoost) classifier. A separate XGBoost model is trained for each user of the system so that behavior specific to each user can be modeled into the drowsiness classifier. We show that user-specific classifiers require very little data and low training time to yield high prediction accuracies in real-time.

Automatic Annotation of Corpora for Emotion Recognition through Facial Expressions Analysis

Alex Mircoli, Claudia Diamantini, Domenico Potena, Emanuele Storti

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Auto-TLDR; Automatic annotation of video subtitles on the basis of facial expressions using machine learning algorithms

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The recent diffusion of social networks has made available an unprecedented amount of user-generated content, which may be analyzed in order to determine people's opinions and emotions about a large variety of topics. Research has made many efforts in defining accurate algorithms for analyzing emotions expressed by users in texts; however, their performance often rely on the existence of large annotated datasets, whose current scarcity represents a major issue. The manual creation of such datasets represents a costly and time-consuming activity and hence there is an increasing demand for techniques for the automatic annotation of corpora. In this work we present a methodology for the automatic annotation of video subtitles on the basis of the analysis of facial expressions of people in videos, with the goal of creating annotated corpora that may be used to train emotion recognition algorithms. Facial expressions are analyzed through machine learning algorithms, on the basis of a set of manually-engineered facial features that are extracted from video frames. The soundness of the proposed methodology has been evaluated through an extensive experimentation aimed at determining the performance on real datasets of each methodological step.

Rotational Adjoint Methods for Learning-Free 3D Human Pose Estimation from IMU Data

Caterina Emilia Agelide Buizza, Yiannis Demiris

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Auto-TLDR; Learning-free 3D Human Pose Estimation from Inertial Measurement Unit Data

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We present a new framework for learning-free 3D human pose estimation from Inertial Measurement Unit (IMU) data. The proposed method does not rely on a full motion sequence to calculate a pose for any particular time point and thus can operate in real-time. A cost function based only on joint rotations is used, removing the need for frequent transformations between rotations and 3D Cartesian coordinates. A Jacobian that preserves skeleton structure is derived using Adjoint methods from Variational Data Assimilation. To facilitate further research in IMU-based Motion Capture, we provide a dataset that combines RGB and depth images from an Intel RealSense camera, marker-based motion capture from an Optitrack system and Xsens IMU data. We have evaluated our method on both our dataset and the Total Capture dataset, showing an average error across 24 joints of 0.45 and 0.48 radians respectively.

Weight Estimation from an RGB-D Camera in Top-View Configuration

Marco Mameli, Marina Paolanti, Nicola Conci, Filippo Tessaro, Emanuele Frontoni, Primo Zingaretti

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Auto-TLDR; Top-View Weight Estimation using Deep Neural Networks

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The development of so-called soft-biometrics aims at providing information related to the physical and behavioural characteristics of a person. This paper focuses on bodyweight estimation based on the observation from a top-view RGB-D camera. In fact, the capability to estimate the weight of a person can be of help in many different applications, from health-related scenarios to business intelligence and retail analytics. To deal with this issue, a TVWE (Top-View Weight Estimation) framework is proposed with the aim of predicting the weight. The approach relies on the adoption of Deep Neural Networks (DNNs) that have been trained on depth data. Each network has also been modified in its top section to replace classification with prediction inference. The performance of five state-of-art DNNs has been compared, namely VGG16, ResNet, Inception, DenseNet and Efficient-Net. In addition, a convolutional auto-encoder has also been included for completeness. Considering the limited literature in this domain, the TVWE framework has been evaluated on a new publicly available dataset: “VRAI Weight estimation Dataset”, which also collects, for each subject, labels related to weight, gender, and height. The experimental results have demonstrated that the proposed methods are suitable for this task, bringing different and significant insights for the application of the solution in different domains.

Estimating Gaze Points from Facial Landmarks by a Remote Spherical Camera

Shigang Li

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Auto-TLDR; Gaze Point Estimation from a Spherical Image from Facial Landmarks

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From a spherical image, a gaze point, instead of gaze vectors, can be estimated directly because a remote spherical camera can observe a user's face and a gaze target simultaneously. This paper investigates the problem of estimating a gaze point in a spherical image from facial landmarks. In contrast with the existing methods which usually assume gaze points move on a narrow plane, the proposed method can cope with the situation where gaze points vary in depth for a relatively wide field of view. As shown in the results of comparative experiments, we find the orthogonal coordinates of facial landmarks on a unit sphere is a reasonable representation in comparison with spherical polar coordinates; the cues of head pose is helpful to improve the accuracy of gaze points. Consequently, the proposed method achieves a performance on the accuracy of gaze points estimation which is comparable to the state of the art methods.

Anomaly Detection, Localization and Classification for Railway Inspection

Riccardo Gasparini, Andrea D'Eusanio, Guido Borghi, Stefano Pini, Giuseppe Scaglione, Simone Calderara, Eugenio Fedeli, Rita Cucchiara

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Auto-TLDR; Anomaly Detection and Localization using thermal images in the lowlight environment

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The ability to detect, localize and classify objects that are anomalies is a challenging task in the computer vision community. In this paper, we tackle these tasks developing a framework to automatically inspect the railway during the night. Specifically, it is able to predict the presence, the image coordinates and the class of obstacles. To deal with the lowlight environment, the framework is based on thermal images and consists of three different modules that address the problem of detecting anomalies, predicting their image coordinates and classifying them. Moreover, due to the absolute lack of publicly released datasets collected in the railway context for anomaly detection, we introduce a new multi-modal dataset, acquired from a rail drone, used to evaluate the proposed framework. Experimental results confirm the accuracy of the framework and its suitability, in terms of computational load, performance, and inference time, to be implemented on a self-powered inspection system.

RISEdb: A Novel Indoor Localization Dataset

Carlos Sanchez Belenguer, Erik Wolfart, Álvaro Casado Coscollá, Vitor Sequeira

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Auto-TLDR; Indoor Localization Using LiDAR SLAM and Smartphones: A Benchmarking Dataset

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In this paper we introduce a novel public dataset for developing and benchmarking indoor localization systems. We have selected and 3D mapped a set of representative indoor environments including a large office building, a conference room, a workshop, an exhibition area and a restaurant. Our acquisition pipeline is based on a portable LiDAR SLAM backpack to map the buildings and to accurately track the pose of the user as it moves freely inside them. We introduce the calibration procedures that enable us to acquire and geo-reference live data coming from different independent sensors rigidly attached to the backpack. This has allowed us to collect long sequences of spherical and stereo images, together with all the sensor readings coming from a consumer smartphone and locate them inside the map with centimetre accuracy. The dataset addresses many of the limitations of existing indoor localization datasets regarding the scale and diversity of the mapped buildings; the number of acquired sequences under varying conditions; the accuracy of the ground-truth trajectory; the availability of a detailed 3D model and the availability of different sensor types. It enables the benchmarking of existing and the development of new indoor localization approaches, in particular for deep learning based systems that require large amounts of labeled training data.

IPT: A Dataset for Identity Preserved Tracking in Closed Domains

Thomas Heitzinger, Martin Kampel

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Auto-TLDR; Identity Preserved Tracking Using Depth Data for Privacy and Privacy

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We present a public dataset for Identity Preserved Tracking (IPT) consisting of sequences of depth data recorded using an Orbbec Astra depth sensor. The dataset features sequences in ten different locations with a high amount of background variation and is designed to be applicable to a wide range of tasks. Its labeling is versatile, allowing for tracking in either 3d space or image coordinates. Next to frame-by-frame 3d and inferred bounding box labeling we provide supplementary annotation of camera poses and room layouts, split in multiple semantically distinct categories. Intended use-cases are applications where both a high level understanding of scene understanding and privacy are central points of consideration, such as active and assisted living (AAL), security and industrial safety. Compared to similar public datasets IPT distinguishes itself with its sequential data format, 3d instance labeling and room layout annotation. We present baseline object detection results in image coordinates using a YOLOv3 network architecture and implement a background model suitable for online tracking applications to increase detection accuracy. Additionally we propose a novel volumetric non-maximum suppression (V-NMS) approach, taking advantage of known room geometry. Last we provide baseline person tracking results utilizing Multiple Object Tracking Challenge (MOTChallenge) evaluation metrics of the CVPR19 benchmark.

Joint Face Alignment and 3D Face Reconstruction with Efficient Convolution Neural Networks

Keqiang Li, Huaiyu Wu, Xiuqin Shang, Zhen Shen, Gang Xiong, Xisong Dong, Bin Hu, Fei-Yue Wang

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Auto-TLDR; Mobile-FRNet: Efficient 3D Morphable Model Alignment and 3D Face Reconstruction from a Single 2D Facial Image

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3D face reconstruction from a single 2D facial image is a challenging and concerned problem. Recent methods based on CNN typically aim to learn parameters of 3D Morphable Model (3DMM) from 2D images to render face alignment and 3D face reconstruction. Most algorithms are designed for faces with small, medium yaw angles, which is extremely challenging to align faces in large poses. At the same time, they are not efficient usually. The main challenge is that it takes time to determine the parameters accurately. In order to address this challenge with the goal of improving performance, this paper proposes a novel and efficient end-to-end framework. We design an efficient and lightweight network model combined with Depthwise Separable Convolution and Muti-scale Representation, Lightweight Attention Mechanism, named Mobile-FRNet. Simultaneously, different loss functions are used to constrain and optimize 3DMM parameters and 3D vertices during training to improve the performance of the network. Meanwhile, extensive experiments on the challenging datasets show that our method significantly improves the accuracy of face alignment and 3D face reconstruction. The model parameters and complexity of our method are also improved greatly.

Learning Visual Voice Activity Detection with an Automatically Annotated Dataset

Stéphane Lathuiliere, Pablo Mesejo, Radu Horaud

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Auto-TLDR; Deep Visual Voice Activity Detection with Optical Flow

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Visual voice activity detection (V-VAD) uses visual features to predict whether a person is speaking or not. V-VAD is useful whenever audio VAD (A-VAD) is inefficient either because the acoustic signal is difficult to analyze or is simply missing. We propose two deep architectures for V-VAD, one based on facial landmarks and one based on optical flow. Moreover, available datasets, used for learning and for testing V-VAD, lack content variability. We introduce a novel methodology to automatically create and annotate very large datasets in-the-wild, based on combining A-VAD and face detection. A thorough empirical evaluation shows the advantage of training the proposed deep V-VAD models with such a dataset.

Adaptive Feature Fusion Network for Gaze Tracking in Mobile Tablets

Yiwei Bao, Yihua Cheng, Yunfei Liu, Feng Lu

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Auto-TLDR; Adaptive Feature Fusion Network for Multi-stream Gaze Estimation in Mobile Tablets

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Recently, many multi-stream gaze estimation methods have been proposed. They estimate gaze from eye and face appearances and achieve reasonable accuracy. However, most of the methods simply concatenate the features extracted from eye and face appearance. The feature fusion process has been ignored. In this paper, we propose a novel Adaptive Feature Fusion Network (AFF-Net), which performs gaze tracking task in mobile tablets. We stack two-eye feature maps and utilize Squeeze-and-Excitation layers to adaptively fuse two-eye features based on different eye features. Meanwhile, we also propose Adaptive Group Normalization to recalibrate eye features with the guidance of face appearance characteristics. Extensive experiments on both GazeCapture and MPIIFaceGaze datasets demonstrate consistently superior performance of the proposed method.

Occlusion-Tolerant and Personalized 3D Human Pose Estimation in RGB Images

Ammar Qammaz, Antonis Argyros

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Auto-TLDR; Real-Time 3D Human Pose Estimation in BVH using Inverse Kinematics Solver and Neural Networks

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We introduce a real-time method that estimates the 3D human pose directly in the popular BVH format, given estimations of the 2D body joints in RGB images. Our contributions include: (a) A novel and compact 2D pose representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the H3.6M dataset compared to the baseline MocapNET method while maintaining real-time performance (70 fps in CPU-only execution).

Motion-Supervised Co-Part Segmentation

Aliaksandr Siarohin, Subhankar Roy, Stéphane Lathuiliere, Sergey Tulyakov, Elisa Ricci, Nicu Sebe

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Auto-TLDR; Self-supervised Co-Part Segmentation Using Motion Information from Videos

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Recent co-part segmentation methods mostly operate in a supervised learning setting, which requires a large amount of annotated data for training. To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation. Differently from previous works, our approach develops the idea that motion information inferred from videos can be leveraged to discover meaningful object parts. To this end, our method relies on pairs of frames sampled from the same video. The network learns to predict part segments together with a representation of the motion between two frames, which permits reconstruction of the target image. Through extensive experimental evaluation on publicly available video sequences we demonstrate that our approach can produce improved segmentation maps with respect to previous self-supervised co-part segmentation approaches.

Siamese-Structure Deep Neural Network Recognizing Changes in Facial Expression According to the Degree of Smiling

Kazuaki Kondo, Taichi Nakamura, Yuichi Nakamura, Shin'Ichi Satoh

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Auto-TLDR; A Siamese-Structure Deep Neural Network for Happiness Recognition

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A smile is a representative expression of happiness or high quality-of-life; however, automatic recognition of a smile according to happiness remains a challenging task. Because expressions of happiness are strongly dependent upon physical condition and occurrence of other emotions, and similar facial expression often occur under different emotions, we consider that there is no absolute visual pattern of a smile corresponding to happiness. Therefore, in this study, we assumed that a ``smile with happiness'' is observed as the temporal ascent in the degree of smiling and attempted to recognize this by capturing changes in facial expression within temporally sequential images. As an implementation of this scheme, we proposed a Siamese-structure deep neural network to compare facial expressions in two input images and estimate the existence of smile ascension or descension. For primal analysis of the proposed network, we developed a unique smiling dataset containing image pairs with various changes in smiling degree, including slight changes. The results demonstrated that the proposed method achieved nearly perfect recognition with >0.95 accuracy when recognizing changes in the degree of smiling that humans certainly recognize. Attention regions that contributed to the predicted labels were concentrated on the mouth, cheeks, and tail of the eyes, which indicates a reasonable function for recognizing changes in smiling degree was constructed by the proposed method.

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.

Facial Expression Recognition Using Residual Masking Network

Luan Pham, Vu Huynh, Tuan Anh Tran

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Auto-TLDR; Deep Residual Masking for Automatic Facial Expression Recognition

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Automatic facial expression recognition (FER) has gained much attention due to its applications in human-computer interaction. Among the approaches to improve FER tasks, this paper focuses on deep architecture with the attention mechanism. We propose a novel Masking idea to boost the performance of CNN in facial expression task. It uses a segmentation network to refine feature maps, enabling the network to focus on relevant information to make correct decisions. In experiments, we combine the ubiquitous Deep Residual Network and Unet-like architecture to produce a Residual Masking Network. The proposed method holds state-of-the-art (SOTA) accuracy on the well-known FER2013 and private VEMO datasets. Our works are available on Github.

User-Independent Gaze Estimation by Extracting Pupil Parameter and Its Mapping to the Gaze Angle

Sang Yoon Han, Nam Ik Cho

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Auto-TLDR; Gaze Point Estimation using Pupil Shape for Generalization

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Since gaze estimation plays a crucial role in recognizing human intentions, it has been researched for a long time, and its accuracy is ever increasing. However, due to the wide variation in eye shapes and focusing abilities between the individuals, accuracies of most algorithms vary depending on each person in the test group, especially when the initial calibration is not well performed. To alleviate the user-dependency, we attempt to derive features that are general for most people and use them as the input to a deep network instead of using the images as the input. Specifically, we use the pupil shape as the core feature because it is directly related to the 3D eyeball rotation, and thus the gaze direction. While existing deep learning methods learn the gaze point by extracting various features from the image, we focus on the mapping function from the eyeball rotation to the gaze point by using the pupil shape as the input. It is shown that the accuracy of gaze point estimation also becomes robust for the uncalibrated points by following the characteristics of the mapping function. Also, our gaze network learns the gaze difference to facilitate the re-calibration process to fix the calibration-drift problem that typically occurs with glass-type or head-mount devices.

Better Prior Knowledge Improves Human-Pose-Based Extrinsic Camera Calibration

Olivier Moliner, Sangxia Huang, Kalle Åström

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Auto-TLDR; Improving Human-pose-based Extrinsic Calibration for Multi-Camera Systems

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Accurate extrinsic calibration of wide baseline multi-camera systems enables better understanding of 3D scenes for many applications and is of great practical importance. Classical Structure-from-Motion calibration methods require special calibration equipment so that accurate point correspondences can be detected between different views. In addition, an operator with some training is usually needed to ensure that data is collected in a way that leads to good calibration accuracy. This limits the ease of adoption of such technologies. Recently, methods have been proposed to use human pose estimation models to establish point correspondences, thus removing the need for any special equipment. The challenge with this approach is that human pose estimation algorithms typically produce much less accurate feature points compared to classical patch-based methods. Another problem is that ambient human motion might not be optimal for calibration. We build upon prior works and introduce several novel ideas to improve the accuracy of human-pose-based extrinsic calibration. Our first contribution is a robust reprojection loss based on a better understanding of the sources of pose estimation error. Our second contribution is a 3D human pose likelihood model learned from motion capture data. We demonstrate significant improvements in calibration accuracy by evaluating our method on four publicly available datasets.

Light3DPose: Real-Time Multi-Person 3D Pose Estimation from Multiple Views

Alessio Elmi, Davide Mazzini, Pietro Tortella

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Auto-TLDR; 3D Pose Estimation of Multiple People from a Few calibrated Camera Views using Deep Learning

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We present an approach to perform 3D pose estimation of multiple people from a few calibrated camera views. Our architecture, leveraging the recently proposed unprojection layer, aggregates feature-maps from a 2D pose estimator backbone into a comprehensive representation of the 3D scene. Such intermediate representation is then elaborated by a fully-convolutional volumetric network and a decoding stage to extract 3D skeletons with sub-voxel accuracy. Our method achieves state of the art MPJPE on the CMU Panoptic dataset using a few unseen views and obtains competitive results even with a single input view. We also assess the transfer learning capabilities of the model by testing it against the publicly available Shelf dataset obtaining good performance metrics. The proposed method is inherently efficient: as a pure bottom-up approach, it is computationally independent of the number of people in the scene. Furthermore, even though the computational burden of the 2D part scales linearly with the number of input views, the overall architecture is able to exploit a very lightweight 2D backbone which is orders of magnitude faster than the volumetric counterpart, resulting in fast inference time. The system can run at 6 FPS, processing up to 10 camera views on a single 1080Ti GPU.

StrongPose: Bottom-up and Strong Keypoint Heat Map Based Pose Estimation

Niaz Ahmad, Jongwon Yoon

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Auto-TLDR; StrongPose: A bottom-up box-free approach for human pose estimation and action recognition

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Adaptation of deep convolutional neural network has made revolutionary progress in human pose estimation, various applications in recent years have drawn considerable attention. However, prediction and localization of the keypoints in single and multi-person images are a challenging problem. Towards this purpose, we present a bottom-up box-free approach for the task of pose estimation and action recognition. We proposed a StrongPose system model that uses part-based modeling to tackle object-part associations. The model utilizes a convolution network that learns how to detect Strong Keypoints Heat Maps (SKHM) and predict their comparative displacements, enabling us to group keypoints into person pose instances. Further, we produce Body Heat Maps (BHM) with the help of keypoints which allows us to localize the human body in the picture. The StrongPose framework is based on fully-convolutional engineering and permits proficient inference, with runtime basically autonomous of the number of individuals display within the scene. Train and test on COCO data alone, our framework achieves COCO test-dev keypoint average precision of 0.708 using ResNet-101 and 0.725 using ResNet-152, which considerably outperforms all prior bottom-up pose estimation frameworks.

LFIR2Pose: Pose Estimation from an Extremely Low-Resolution FIR Image Sequence

Saki Iwata, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Tomoyoshi Aizawa

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Auto-TLDR; LFIR2Pose: Human Pose Estimation from a Low-Resolution Far-InfraRed Image Sequence

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In this paper, we propose a method for human pose estimation from a Low-resolution Far-InfraRed (LFIR) image sequence captured by a 16 × 16 FIR sensor array. Human body estimation from such a single LFIR image is a hard task. For training the estimation model, annotation of the human pose to the images is also a difficult task for human. Thus, we propose the LFIR2Pose model which accepts a sequence of LFIR images and outputs the human pose of the last frame, and also propose an automatic annotation system for the model training. Additionally, considering that the scale of human body motion is largely different among body parts, we also propose a loss function focusing on the difference. Through an experiment, we evaluated the human pose estimation accuracy using an original data set, and confirmed that human pose can be estimated accurately from an LFIR image sequence.

Recognizing American Sign Language Nonmanual Signal Grammar Errors in Continuous Videos

Elahe Vahdani, Longlong Jing, Ying-Li Tian, Matt Huenerfauth

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Auto-TLDR; ASL-HW-RGBD: Recognizing Grammatical Errors in Continuous Sign Language

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As part of the development of an educational tool that can help students achieve fluency in American Sign Language (ASL) through independent and interactive practice with immediate feedback, this paper introduces a near real-time system to recognize grammatical errors in continuous signing videos without necessarily identifying the entire sequence of signs. Our system automatically recognizes if a performance of ASL sentences contains grammatical errors made by ASL students. We first recognize the ASL grammatical elements including both manual gestures and nonmanual signals independently from multiple modalities (i.e. hand gestures, facial expressions, and head movements) by 3D-ResNet networks. Then the temporal boundaries of grammatical elements from different modalities are examined to detect ASL grammatical mistakes by using a sliding window-based approach. We have collected a dataset of continuous sign language, ASL-HW-RGBD, covering different aspects of ASL grammars for training and testing. Our system is able to recognize grammatical elements on ASL-HW-RGBD from manual gestures, facial expressions, and head movements and successfully detect 8 ASL grammatical mistakes.

Detecting Manipulated Facial Videos: A Time Series Solution

Zhang Zhewei, Ma Can, Gao Meilin, Ding Bowen

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Auto-TLDR; Face-Alignment Based Bi-LSTM for Fake Video Detection

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We propose a new method to expose fake videos based on a time series solution. The method is based on bidirectional long short-term memory (Bi-LSTM) backbone architecture with two different types of features: {Face-Alignment} and {Dense-Face-Alignment}, in which both of them are physiological signals that can be distinguished between fake and original videos. We choose 68 landmark points as the feature of {Face-Alignment} and Pose Adaptive Feature (PAF) for {Dense-Face-Alignment}. Based on these two facial features, we designed two deep networks. In addition, we optimize our network by adding an attention mechanism that improves detection precision. Our method is tested over benchmarks of Face Forensics/Face Forensics++ dataset and show a promising performance on inference speed while maintaining accuracy with state-of art solutions that deal against DeepFake.

Boundary Guided Image Translation for Pose Estimation from Ultra-Low Resolution Thermal Sensor

Kohei Kurihara, Tianren Wang, Teng Zhang, Brian Carrington Lovell

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Auto-TLDR; Pose Estimation on Low-Resolution Thermal Images Using Image-to-Image Translation Architecture

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This work addresses the pose estimation task on low-resolution images captured using thermal sensors which can operate in a no-light environment. Low-resolution thermal sensors have been widely adopted in various applications for cost control and privacy protection purposes. In this paper, targeting the challenging scenario of ultra-low resolution thermal imaging (3232 pixels), we aim to estimate human poses for the purpose of monitoring health conditions and indoor events. To overcome the challenges in ultra-low resolution thermal imaging such as blurred boundaries and data scarcity, we propose a new Image-to-Image (I2I) translation architecture which can translate the original blurred thermal image into a visible light image with sharper boundaries. Then the generated visible light image can be fed into the off-the-shelf pose estimator which was well-trained in the visible domain. Experimental results suggest that the proposed framework outperforms other state-of-the-art methods in the I2I based pose estimation task for our thermal image dataset. Furthermore, we also demonstrated the merits of the proposed method on the publicly available FLIR dataset by measuring the quality of translated images.

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

Jun Weng, Yang Yang, Zichang Tan, Zhen Lei

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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.

Responsive Social Smile: A Machine-Learning Based Multimodal Behavior Assessment Framework towards Early Stage Autism Screening

Yueran Pan, Kunjing Cai, Ming Cheng, Xiaobing Zou, Ming Li

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Auto-TLDR; Responsive Social Smile: A Machine Learningbased Assessment Framework for Early ASD Screening

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder, which causes social deficits in social lives. Early ASD screening for children is an important method to reduce the impact of ASD on people’s whole lives. Traditional screening methods rely on protocol experiments and subjective evaluations from clinicians and domain experts and thereby cost a lot. To standardize the process of ASD screening, we 1 collaborate with a group of ASD experts, and design a ”Responsive Social Smile” protocol and an experiment environment. Also, we propose a machine learningbased assessment framework for early ASD screening. By integrating technologies of speech recognition and computer vision, the framework can quantitatively analyze the behaviors of children under well-designed protocols. By collecting 196 test samples from 41 children in the clinical treatments, our proposed method obtains 85.20% accuracy for the score prediction of individual protocol, and 80.49% unweighted accuracy for the final ASD prediction. This result indicates that our model reaches the average level of domain experts in ASD diagnosis.

Depth Videos for the Classification of Micro-Expressions

Ankith Jain Rakesh Kumar, Bir Bhanu, Christopher Casey, Sierra Cheung, Aaron Seitz

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Auto-TLDR; RGB-D Dataset for the Classification of Facial Micro-expressions

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Facial micro-expressions are spontaneous, subtle, involuntary muscle movements occurring briefly on the face. The spotting and recognition of these expressions are difficult due to the subtle behavior, and the time duration of these expressions is about half a second, which makes it difficult for humans to identify them. These micro-expressions have many applications in our daily life, such as in the field of online learning, game playing, lie detection, and therapy sessions. Traditionally, researchers use RGB images/videos to spot and classify these micro-expressions, which pose challenging problems, such as illumination, privacy concerns and pose variation. The use of depth videos solves these issues to some extent, as the depth videos are not susceptible to the variation in illumination. This paper describes the collection of a first RGB-D dataset for the classification of facial micro-expressions into 6 universal expressions: Anger, Happy, Sad, Fear, Disgust, and Surprise. This paper shows the comparison between the RGB and Depth videos for the classification of facial micro-expressions. Further, a comparison of results shows that depth videos alone can be used to classify facial micro-expressions correctly in a decision tree structure by using the traditional and deep learning approaches with good classification accuracy. The dataset will be released to the public in the near future.

Audio-Video Detection of the Active Speaker in Meetings

Francisco Madrigal, Frederic Lerasle, Lionel Pibre, Isabelle Ferrané

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Auto-TLDR; Active Speaker Detection with Visual and Contextual Information from Meeting Context

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Meetings are a common activity that provides certain challenges when creating systems that assist them. Such is the case of the Active speaker detection, which can provide useful information for human interaction modeling, or human-robot interaction. Active speaker detection is mostly done using speech, however, certain visual and contextual information can provide additional insights. In this paper we propose an active speaker detection framework that integrates audiovisual features with social information, from the meeting context. Visual cue is processed using a Convolutional Neural Network (CNN) that captures the spatio-temporal relationships. We analyze several CNN architectures with both cues: raw pixels (RGB images) and motion (estimated with optical flow). Contextual reasoning is done with an original methodology, based on the gaze of all participants. We evaluate our proposal with a public \textcolor{black}{benchmark} in state-of-art: AMI corpus. We show how the addition of visual and context information improves the performance of the active speaker detection.

Two-Stream Temporal Convolutional Network for Dynamic Facial Attractiveness Prediction

Nina Weng, Jiahao Wang, Annan Li, Yunhong Wang

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Auto-TLDR; 2S-TCN: A Two-Stream Temporal Convolutional Network for Dynamic Facial Attractiveness Prediction

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In the field of facial attractiveness prediction, while deep models using static pictures have shown promising results, little attention is paid to dynamic facial information, which is proven to be influential by psychological studies. Meanwhile, the increasing popularity of short video apps creates an enormous demand of facial attractiveness prediction from short video clips. In this paper, we target on the dynamic facial attractiveness prediction problem. To begin with, a large-scale video-based facial attractiveness prediction dataset (VFAP) with more than one thousand clips from TikTok is collected. A two-stream temporal convolutional network (2S-TCN) is then proposed to capture dynamic attractiveness feature from both facial appearance and landmarks. We employ attentive feature enhancement along with specially designed modality and temporal fusion strategies to better explore the temporal dynamics. Extensive experiments on the proposed VFAP dataset demonstrate that 2S-TCN has a distinct advantage over the state-of-the-art static prediction methods.

Learning Emotional Blinded Face Representations

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

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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.

One Step Clustering Based on A-Contrario Framework for Detection of Alterations in Historical Violins

Alireza Rezaei, Sylvie Le Hégarat-Mascle, Emanuel Aldea, Piercarlo Dondi, Marco Malagodi

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Auto-TLDR; A-Contrario Clustering for the Detection of Altered Violins using UVIFL Images

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Preventive conservation is an important practice in Cultural Heritage. The constant monitoring of the state of conservation of an artwork helps us reduce the risk of damage and number of interventions necessary. In this work, we propose a probabilistic approach for the detection of alterations on the surface of historical violins based on an a-contrario framework. Our method is a one step NFA clustering solution which considers grey-level and spatial density information in one background model. The proposed method is robust to noise and avoids parameter tuning and any assumption about the quantity of the worn out areas. We have used as input UV induced fluorescence (UVIFL) images for considering details not perceivable with visible light. Tests were conducted on image sequences included in the ``Violins UVIFL imagery'' dataset. Results illustrate the ability of the algorithm to distinguish the worn area from the surrounding regions. Comparisons with the state of the art clustering methods shows improved overall precision and recall.

Domain Siamese CNNs for Sparse Multispectral Disparity Estimation

David-Alexandre Beaupre, Guillaume-Alexandre Bilodeau

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Auto-TLDR; Multispectral Disparity Estimation between Thermal and Visible Images using Deep Neural Networks

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Multispectral disparity estimation is a difficult task for many reasons: it as all the same challenges as traditional visible-visible disparity estimation (occlusions, repetitive patterns, textureless surfaces), in addition of having very few common visual information between images (e.g. color information vs. thermal information). In this paper, we propose a new CNN architecture able to do disparity estimation between images from different spectrum, namely thermal and visible in our case. Our proposed model takes two patches as input and proceeds to do domain feature extraction for each of them. Features from both domains are then merged with two fusion operations, namely correlation and concatenation. These merged vectors are then forwarded to their respective classification heads, which are responsible for classifying the inputs as being same or not. Using two merging operations gives more robustness to our feature extraction process, which leads to more precise disparity estimation. Our method was tested using the publicly available LITIV 2014 and LITIV 2018 datasets, and showed best results when compared to other 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.

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.

Multi-View Object Detection Using Epipolar Constraints within Cluttered X-Ray Security Imagery

Brian Kostadinov Shalon Isaac-Medina, Chris G. Willcocks, Toby Breckon

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Auto-TLDR; Exploiting Epipolar Constraints for Multi-View Object Detection in X-ray Security Images

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Automatic detection for threat object items is an increasing emerging area of future application in X-ray security imagery. Although modern X-ray security scanners can provide two or more views, the integration of such object detectors across the views has not been widely explored with rigour. Therefore, we investigate the application of geometric constraints using the epipolar nature of multi-view imagery to improve object detection performance. Furthermore, we assume that images come from uncalibrated views, such that a method to estimate the fundamental matrix using ground truth bounding box centroids from multiple view object detection labels is proposed. In addition, detections are given a score based on its similarity with respect to the distribution of the error of the epipolar estimation. This score is used as confidence weights for merging duplicated predictions using non-maximum suppression. Using a standard object detector (YOLOv3), our technique increases the average precision of detection by 2.8% on a dataset composed of firearms, laptops, knives and cameras. These results indicate that the integration of images at different views significantly improves the detection performance of threat items of cluttered X-ray security images.

Quantified Facial Temporal-Expressiveness Dynamics for Affect Analysis

Md Taufeeq Uddin, Shaun Canavan

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Auto-TLDR; quantified facial Temporal-expressiveness Dynamics for quantified affect analysis

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The quantification of visual affect data (e.g. face images) is essential to build and monitor automated affect modeling systems efficiently. Considering this, this work proposes quantified facial Temporal-expressiveness Dynamics (TED) to quantify the expressiveness of human faces. The proposed algorithm leverages multimodal facial features by incorporating static and dynamic information to enable accurate measurements of facial expressiveness. We show that TED can be used for high-level tasks such as summarization of unstructured visual data, expectation from and interpretation of automated affect recognition models. To evaluate the positive impact of using TED, a case study was conducted on spontaneous pain using the UNBC-McMaster spontaneous shoulder pain dataset. Experimental results show the efficacy of using TED for quantified affect analysis.

Shape Consistent 2D Keypoint Estimation under Domain Shift

Levi Vasconcelos, Massimiliano Mancini, Davide Boscaini, Barbara Caputo, Elisa Ricci

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Auto-TLDR; Deep Adaptation for Keypoint Prediction under Domain Shift

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Recent unsupervised domain adaptation methods based on deep architectures have shown remarkable performance not only in traditional classification tasks but also in more complex problems involving structured predictions (e.g. semantic segmentation, depth estimation). Following this trend, in this paper we present a novel deep adaptation framework for estimating keypoints under \textit{domain shift}, i.e. when the training (\textit{source}) and the test (\textit{target}) images significantly differ in terms of visual appearance. Our method seamlessly combines three different components: feature alignment, adversarial training and self-supervision. Specifically, our deep architecture leverages from domain-specific distribution alignment layers to perform target adaptation at the feature level. Furthermore, a novel loss is proposed which combines an adversarial term for ensuring aligned predictions in the output space and a geometric consistency term which guarantees coherent predictions between a target sample and its perturbed version. Our extensive experimental evaluation conducted on three publicly available benchmarks shows that our approach outperforms state-of-the-art domain adaptation methods in the 2D keypoint prediction task.