An aperiodic feature representation for gait recognition incross-viewscenariosfor unconstrained biometrics

交叉视角场景下非约束生物计量的步态识别非周期特征表示

Pattern Analysis and Applications, pp 1-14, First online: 26 March 2015

Abstract: The state-of-the-art gait recognition algorithms require a gait cycle estimation before the feature extraction and are classified as periodic algorithms. Their effectiveness substantially decreases due to errors in detecting gait cycles, which are likely to occur in data acquired in non-controlled conditions. Hence, the main contributions of this paper are: (1) propose an aperiodic gait recognition strategy, where features are extracted without the concept of gait cycle, in case of multi-view scenario; (2) propose the fusion of the different feature subspaces of aperiodic feature representations at score level in cross-view scenarios. The experiments were performed with widely known CASIA Gait database B, which enabled us to draw the following major conclusions, (1) for multi-view scenarios, features extracted from gait sequences of varying length have as much discriminating power as traditional periodic features; (2) for cross-view scenarios, we observed an average improvement of 22 % over the error rates of state-of-the-art algorithms, due to the proposed fusion scheme.

Acomprehensive survey of human action recognitionwithspatio-temporal interest point (STIP) detector

借助时空兴趣点探测器进行人体行为识别的综合研究

The Visual Computer, March 2016, Volume 32, Issue 3, pp 289-306

Abstract:Over the past two decades, human action recognition from video has been an important area of research in computer vision. Its applications include surveillance systems, human–computer interactions and various real-world applications where one of the actor is a human being. A number of review works have been done by several researchers in the context of human action recognition. However, it is found that there is a gap in literature when it comes to methodologies of STIP-based detector for human action recognition. This paper presents a comprehensive review on STIP-based methods for human action recognition. STIP-based detectors are robust in detecting interest points from video in spatio-temporal domain. This paper also summarizes related public datasets useful for comparing performances of various techniques.

Entropy volumes for viewpoint-independent gait recognition

视角无关步态识别之熵量

Machine Vision and Applications, November 2015, Volume 26, Issue 7, pp 1079-1094

Abstract:Gait as biometrics has been widely used for human identification. However, direction changes cause difficulties for most of the gait-recognition systems, due to appearance changes. This study presents an efficient multi-view gait-recognition method that allows curved trajectories on completely unconstrained paths for indoor environments. Our method is based on volumetric reconstructions of humans, aligned along their way. A new gait descriptor, termed as gait entropy volume (GEnV), is also proposed. GEnV focuses on capturing 3D dynamical information of walking humans through the concept of entropy. Our approach does not require the sequence to be split into gait cycles. A GEnV-based signature is computed on the basis of the previous 3D gait volumes. Each signature is classified by a support vector machine, and a majority voting policy is used to smooth and reinforce the classifications results. The proposed approach is experimentally validated on the “AVA Multi-View Gait Dataset (AVAMVG)” and on the “KyushuUniversity4D Gait Database (KY4D)”. The results show that this new approach achieves promising results in the problem of gait recognition on unconstrained paths.

A comprehensive review of past and present vision-based techniques for gait recognition

基于视觉的步态识别技术综述

Multimedia Tools and Applications, October 2014, Volume 72, Issue 3, pp 2833-2869

Abstract:Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available—for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance.

Dynamic Distance-BasedShape Features for Gait Recognition

基于动距的步态识别轮廓特征

Journal of Mathematical Imaging and Vision, November 2014, Volume 50, Issue 3, pp 314-326

Abstract:We propose a novel skeleton-based approach to gait recognition using our Skeleton Variance Image. The core of our approach consists of employing the screened Poisson equation to construct a family of smooth distance functions associated with a given shape. The screened Poisson distance function approximation nicely absorbs and is relatively stable to shape boundary perturbations which allows us to define a rough shape skeleton. We demonstrate how our Skeleton Variance Image is a powerful gait cycle descriptor leading to a significant improvement over the existing state of the art gait recognition rate.