Feature extraction using kernel Laplacian maximum margin criterion

We present a novel scheme of feature extraction, namely kernel Laplacian maximum margin criterion, for face recognition. The proposed method seeks to maximize the difference, rather than the ratio, of the determinant between the between-class Laplacian scatter matrix and within-class Laplacian scatter matrix in the implicit feature space via kernel trick. The proposed method not only can produce nonlinear discriminant features, but also does not need to calculate the inverse within-class Laplacian scatter matrix. Experimental results on ORL, FERET, and AR databases validate the effectiveness of the proposed method.