KSLPP: a new algorithm for face recognition A new face image feature extraction and recognition method based on Kernel Supervised Locality Preserving Projections (KSLPP) is proposed, in which samples are projected into high-dimensional feature spaces by nonlinear mapping, the face manifold local structure information is combined with the labels’ information, and the nonlinear feature of faces for recognition is extracted. The nearest neighborhood algorithm is used to construct classifiers. The proposed method is tested and evaluated using the AT&T face database and Yale face database. Experimental results show that KSLPP is more powerful than Eigenface, Fisherface and Laplacianface for face feature extraction and recognition.
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