Slowly Feature Analysis of Gabor Feature for Face Recognition
Jianbin Gao, Jianping Li, Qi Xia · 2008
Obtaining invariant representation of time varying signals is one of the major problems in object recognition. Recently, a new method that slowly feature analysis (SFA) which can extract invariant features of temporally varying signals is being explored, which is an extension of independent component analysis (ICA) which has been used for extracting facial feature. The technique of SFA can be extended to the field of face recognition easily. The Gabor feature face images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. Theses images can produce pronounced local feature that are most suitable for face recognition. SFA would further reduce redundancy and represent slowly varying features explicitly. These slowly varying features are most useful for subsequent pattern discrimination and associative recall. Making use of the slowly feature method, in this paper, we propose a new face recognition algorithm based on Gabor face feature and slowly varying feature analysis. Results indicate that our algorithm is effective and competitive.