Principal component analysis network (PCANet) is a popular deep learning classification method

which has caused wide attention in the area of computer vision due to its practical applications in face recognition

hand-written digit recognition

texture classification

and object recognitions. On the basis of PCANet. The kernel principal component analysis network (KPCANet) method is proposed for face recognition. The model is constructed by four processing components

including principal component analysis (PCA)

kernel principal component analysis (KPCA)

binary hashing

and block-wise histograms. The performance of the proposed method is evaluated using two public face datasets

i.e.

Extended Yale B database and AR face database. The results show that KPCANet outperforms PCANet method. Especially when the face images have large variations about illuminations and expressions

KPCANet gives better recognition results.