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

The effect of time on gait recognition performance

Many studies have shown that it is possible to recognize people by the way they walk. However, there are a number of covariate factors that affect recognition performance. The time between capturing the gallery and the probe has been reported to affect recognition the most. To date, no study has shown the isolated effect of time, irrespective of other covariates. Here we present the first principled study that examines the effect of elapsed time on gait recognition. Using empirical evidence we show for the first time that elapsed time does not affect recognition significantly in the short to medium term. By controlling the clothing worn by the subjects and the environment, a Correct Classification Rate (CCR) of 95% has been achieved over 9 months, on a dataset of 2280 gait samples. Our results show that gait can be used as a reliable biometric over time and at a distance. We have created a new multimodal temporal database to enable the research community to investigate various gait and face covariates. We have also investigated the effect of different type of clothes, variations in speed and footwear on the recognition performance. We have demonstrated that clothing drastically affects performance regardless of elapsed time and significantly more than any of the other covariates that we have considered here. The research then suggests a move towards developing appearance invariant recognition algorithms. Thi

Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors

This paper presents a gait recognition method which combines spatio-temporal motion characteristics, statistical and physical parameters (referred to as STM-SPP) of a human subject for its classification by analysing shape of the subject's silhouette contours using Procrustes shape analysis (PSA) and elliptic Fourier descriptors (EFDs). STM-SPP uses spatio-temporal gait characteristics and physical parameters of human body to resolve similar dissimilarity scores between probe and gallery sequences obtained by PSA. A part-based shape analysis using EFDs is also introduced to achieve robustness against carrying conditions. The classification results by PSA and EFDs are combined, resolving tie in ranking using contour matching based on Hu moments. Experimental results show STM-SPP outperforms several silhouette-based gait recognition methods

People identification and tracking through fusion of facial and gait features

This paper reviews the contemporary (face, gait, and fusion) computational approaches for automatic human identification at a distance. For remote identification, there may exist large intra-class variations that can affect the performance of face/gait systems substantially. First, we review the face recognition algorithms in light of factors, such as illumination, resolution, blur, occlusion, and pose. Then we introduce several popular gait feature templates, and the algorithms against factors such as shoe, carrying condition, camera view, walking surface, elapsed time, and clothing. The motivation of fusing face and gait, is that, gait is less sensitive to the factors that may affect face (e.g., low resolution, illumination, facial occlusion, etc.), while face is robust to the factors that may affect gait (walking surface, clothing, etc.). We review several most recent face and gait fusion methods with different strategies, and the significant performance gains suggest these two modality are complementary for human identification at a distance

People identification and tracking through fusion of facial and gait features

This paper reviews the contemporary (face, gait, and fusion) computational approaches for automatic human identification at a distance. For remote identification, there may exist large intra-class variations that can affect the performance of face/gait systems substantially. First, we review the face recognition algorithms in light of factors, such as illumination, resolution, blur, occlusion, and pose. Then we introduce several popular gait feature templates, and the algorithms against factors such as shoe, carrying condition, camera view, walking surface, elapsed time, and clothing. The motivation of fusing face and gait, is that, gait is less sensitive to the factors that may affect face (e.g., low resolution, illumination, facial occlusion, etc.), while face is robust to the factors that may affect gait (walking surface, clothing, etc.). We review several most recent face and gait fusion methods with different strategies, and the significant performance gains suggest these two modality are complementary for human identification at a distance

View and clothing invariant gait recognition via 3D human semantic folding

A novel 3-dimensional (3D) human semantic folding is introduced to provide a robust and efficient gait recognition method which is invariant to camera view and clothing style. The proposed gait recognition method comprises three modules: (1) 3D body pose, shape and viewing data estimation network (3D-BPSVeNet); (2) gait semantic parameter folding model; and (3) gait semantic feature refining network. First, 3D-BPSVeNet is constructed based on a convolution gated recurrent unit (ConvGRU) to extract 2-dimensional (2D) to 3D body pose and shape semantic descriptors (2D-3D-BPSDs) from a sequence of gait parsed RGB images. A 3D gait model with virtual dressing is then constructed by morphing the template of 3D body model using the estimated 2D-3D-BPSDs and the recognized clothing styles. The more accurate 2D-3D-BPSDs without clothes are then obtained by using the silhouette similarity function when updating the 3D body model to fit the 2D gait. Second, the intrinsic 2D-3D-BPSDs without interference from clothes are encoded by sparse distributed representation (SDR) to gain the binary gait semantic image (SD-BGSI) in a topographical semantic space. By averaging the SD-BGSIs in a gait cycle, a gait semantic folding image (GSFI) is obtained to give a high-level representation of gait. Third, a gait semantic feature refining network is trained to refine the semantic feature extracted directly from GSFI using three types of prior knowledge, i.e., viewing angles, clothing styles and carrying condition. Experimental analyses on CMU MoBo, CASIA B, KY4D, OU-MVLP and OU-ISIR datasets show a significant performance gain in gait recognition in terms of accuracy and robustness