Dynamic biometrics fusion at feature level for video-based human recognition

This paper proposes a novel human recognition method in video, which combines human face and gait traits<br />using a dynamic multi-modal biometrics fusion scheme. The Fisherface approach is adopted to extract face<br />features, while for gait features, Locality Preserving Projection (LPP) is used to achieve low-dimensional<br />manifold embedding of the temporal silhouette data derived from image sequences. Face and gait features are<br />fused dynamically at feature level based on a distance-driven fusion method. Encouraging experimental results<br />are achieved on the video sequences containing 20 people, which show that dynamically fused features produce<br />a more discriminating power than any individual biometric as well as integrated features built on common static<br />fusion schemes.<br />