This paper presents a new algorithm for human gait recognition based on Spatio-temporal body biometric features using wavelet transforms. The proposed algorithm extracts the Gait cycle depending on the width of boundary box from a sequence of Silhouette images. Gait recognition is based on feature level fusion of three feature vectors: the gait spatio-temporal feature represented by the distances between (feet, knees, hands, shoulders, and height); binary difference between consecutive frames of the silhouette for each leg detected separately based on hamming distance; a vector of statistical parameters captured from the wavelet low frequency domain. The fused feature vector is subjected to dimension reduction using linear discriminate analysis. The Nearest Neighbour with a certain threshold used for classification. The threshold is obtained by experiment from a set of data captured from the CASIA database. We shall demonstrate that our method provides a non-traditional identification based on certain threshold to classify the outsider members as non-classified members.

Date Published: 28 May 2013
PDF: 10 pages
Proc. SPIE 8755, Mobile Multimedia/Image Processing, Security, and Applications 2013, 87550R (28 May 2013); doi: 10.1117/12.2017950

Published in SPIE Proceedings Vol. 8755:
Mobile Multimedia/Image Processing, Security, and Applications 2013
Sos S. Agaian; Sabah A. Jassim; Eliza Yingzi Du, Editor(s)