MAXIMUM VARIANCE DIFFERENCE BASED EMBEDDING APPROACH FOR FACIAL FEATURE EXTRACTION
This paper, presents a novel unsupervised dimensionality reduction approach called variance difference embedding (VDE) for facial feature extraction. The proposed VDE method is derived from maximizing the difference between global variance and local variance, so it can draw the close samples closer and simultaneously making the mutually distant samples even more distant from each other. VDE utilizes the maximum variance difference criterion rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing the sample size problem. The results of the experiments conducted on ORL database, Yale database and a subset of PIE database indicate the effectiveness of the proposed VDE method on facial feature extraction and classification.
maximum variance difference global variance local variance face recognition