Human gait recognition with matrix representation.pdfVIP

896 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 16, NO. 7, JULY 2006 Human Gait Recognition With Matrix Representation Dong Xu, Shuicheng Yan, Dacheng Tao, Lei Zhang, Xuelong Li, and Hong-Jiang Zhang Abstract—Human gait is an important biometric feature. It can be perceived from a great distance and has recently attracted greater attention in video-surveillance-related applications, such as closed-circuit television. We explore gait recognition based on a matrix representation in this paper. First, binary silhouettes over one gait cycle are averaged. As a result, each gait video sequence, containing a number of gait cycles, is represented by a series of gray-level averaged images. Then, a matrix-based unsupervised algorithm, namely coupled subspace analysis (CSA), is employed as a preprocessing step to remove noise and retain the most rep- resentative information. Finally, a supervised algorithm, namely discriminant analysis with tensor representation, is applied to further improve classification ability. This matrix-based scheme demonstrates a much better gait recognition performance than state-of-the-art algorithms on the standard USF HumanID Gait database. Index Terms—Coupled subspaces analysis (CSA), dimension- ality reduction, discriminant analysis with tensor representation (DATER), human gait recognition, object representation. I. INTRODUCTION HUMAN gait recognition has attracted growing attentionin video-surveillance-based applications [13], such as closed-circuit television (CCTV) surveillance, owing to its great potential in the recognition of individuals from a distance. Recent research [13], [20] has shown that individuals have distinctive and special ways of walking and that human gait recognition has many advantages. First, human gait is a bio- metric feature that may be captured from a great distance, and, unlike other traditional biometric features, such as fingerprints and irises, gait has the advantage of being unobtrusive.