Feature extraction of human face using kernel Fisher discriminant
Bingyu Sun · Ha'erbin gongye daxue xuebao · 2009
In this paper,a method employing the kernel Fisher discriminant and wavelet transform to complete the feature extraction for human face recognition is proposed. Compared with several commonly used methods for feature extraction,the proposed method can not only process dimension reduction,but also provide information for classification. Furthermore,it performs well in linearly nonseparable case. So optimal results can be achieved for human face recognition,which is a nonlinear problem. To reduce the computational complexity,the wavelet transform is applied to the pretreatment of original human face images. The experiments on ORL dataset prove the efficiency of the proposed method.