New nonlinear feature extraction method for face recognition
Jingyu Yang · Jisuanji gongcheng yu sheji · 2008
A novel kernel maximum scatter difference discriminant analysis(KMSDA) based on scatter difference criterion is developed for extraction of nolinear feature.The proposed method not only extract nolinear feature for faces but also essentially avoid the difficulties caused by the singularity of kernel within-class scatter matrix in traditional kernel Fisher discriminant analysis(KFDA).In addition,much computational time is saved due to its lower computational complexity.The experimental results on ORL face database verify the effectiveness of the proposed method.