Project Description

This work proposes an innovative gait identification and authentication method based on the use of novel 2-D and 3-D features. Depthrelated data are assigned to the binary image silhouette sequences using two new transforms: the 3-D radial silhouette distribution transform and the 3-D geodesic silhouette distribution transform. Furthermore, the use of a genetic algorithm is presented for fusing information from different feature extractors. Specifically, three new feature extraction techniques are proposed: the two of them are based on the generalized radon transform, namely the radial integration transform and the circular integration transform, and the third is based on the weighted Krawtchouk moments. Extensive experiments carried out on USF “Gait Challenge” and proprietary HUMABIO gait database demonstrate the validity of the proposed scheme.

D. Ioannidis, D. Tzovaras, I.G. Damousis, S. Argyropoulos and K. Moustakas. “Gait Recognition using Compact Feature Extraction Transforms and Depth Information”, IEEE Transactions on Information Forensics and Security, vol. 2, no. 3, pp. 623-630, September 2007.