Modified kernel-based nonlinear feature extraction [face recognition example]

Feature extraction techniques are widely used in many applications to pre-process data in order to reduce the complexity of subsequent processes. A group of kernel-based Fisher discriminant analysis (KFDA) algorithms has attracted much attention due to their high performance. In this paper, the inherent limitations of those KFDA algorithms have been discussed and a novel algorithm is proposed to effectively overcome those limitations. Experimental results on face recognition suggest that this proposed algorithm is superior to the existing methods in terms of correct classification rate.