Recurrent Attention Models for Depth-Based Person Identification

We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark. Our approach leverages unique 4D spatio-temporal signatures to address the identification problem across days. Formulated as a reinforcement learning task, our model is based on a combination of convolutional and recurrent neural networks with the goal of identifying small, discriminative regions indicative of human identity. We demonstrate that our model produces state-of-the-art results on several published datasets given only depth images. We further study the robustness of our model towards viewpoint, appearance, and volumetric changes. Finally, we share insights gleaned from interpretable 2D, 3D, and 4D visualizations of our model's spatio-temporal attention.Comment: Computer Vision and Pattern Recognition (CVPR) 201

Action Recognition in Video by Covariance Matching of Silhouette Tunnels

Abstract—Action recognition is a challenging problem in video analytics due to event complexity, variations in imaging conditions, and intra- and inter-individual action-variability. Central to these challenges is the way one models actions in video, i.e., action representation. In this paper, an action is viewed as a temporal sequence of local shape-deformations of centroid-centered object silhouettes, i.e., the shape of the centroid-centered object silhouette tunnel. Each action is rep-resented by the empirical covariance matrix of a set of 13-dimensional normalized geometric feature vectors that capture the shape of the silhouette tunnel. The similarity of two actions is measured in terms of a Riemannian metric between their covariance matrices. The silhouette tunnel of a test video is broken into short overlapping segments and each segment is classified using a dictionary of labeled action covariance matrices and the nearest neighbor rule. On a database of 90 short video sequences this attains a correct classification rate of 97%, which is very close to the state-of-the-art, at almost 5-fold reduced computational cost. Majority-vote fusion of segment decisions achieves 100 % classification rate. Keywords-video analysis; action recognition; silhouette tun-nel; covariance matching; generalized eigenvalues; I

Appropriateness of gait analysis for biometrics: Initial study using FDA method

Human body movement has been under continuous research for many years due to its potential application as a novel biometric system to identify individuals. It is possible to utilize various techniques, not only to obtain requested movement data, but also to analyse movement data. This paper uses functional data analysis on data obtained from 12 volunteers and uses 20 markers from the 3D motion capture system VICON MX T020. The functional data analysis was chosen as a suitable tool to obtain more information about an individual's movement because it uses a technique for real-time data, which corresponds to continuous time process. The results show that all markers, under any walking speed and condition, identify a significantly high percentage of individual pairs. Further, our results discriminate between markers, where some markers are highly dependent on walking speed and condition, and also on the influence of body part asymmetry. In addition, regular movement patterns in almost all participants’ data shows a potential to identify individuals based on gait recognition with a 1:1 matching result. © 2017 Elsevier LtdIGA grant at Tomas Bata University in Zlin [IGA/FAI/2013/001]; European Regional Development Fund under the project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Grant Agency of the Czech Republic [GA15-06991S]; Scientific Grant Agency of the Ministry of Education of the Slovak Republic; Slovak Academy of Sciences [VEGA 2/0011/16]; Slovak Research and Development Agency [APVV-15-0295

Gait recognition based on shape and motion analysis of silhouette contours

This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject’s silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject’s shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject’s back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject’s leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods

Facial movement based human user authentication

Face recognition is a form of biometric authentication that has received significant attention during the last decades. Using the human face as a key to security, face recognition technology can be potentially employed in many commercial and law enforcement applications. Despite of the fact that most of the face recognition techniques have greatly developed since the earliest forms, they suffer from spoofing attack which aims at deceiving the sensor by manipulating a face replica. One of the methods to solve this problem is to utilize facial movements. Facial muscle movements represent facial behavior which makes it unrealistic to be replicated and thus more distinctive. The third dimension of facial data - depth - is also utilized to improve recognition performance and to avoid video-based attack. Apart from security concerns, physiologists and psychologists have discovered the imperative role of facial movements during human face perception. Therefore, a 3D dynamic signature can be added to augment facial recognition for which relying on static features related to shape and color. In this thesis, a user authentication method based on spatiotemporal facial movements is proposed. Facial movements are obtained by making a facial expression in front of a 3D camera and are encoded by a standard system. By discretizing motion classifications into values, the problem of face recognition can be reinterpreted as matching two time sequences - probe and gallery - for each facial movement category obtained during the enrollment phase and the verification phase. Experiments have been conducted to show the possibility of discriminating subjects based on their facial movements

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers