Local graph embedding based on maximum margin criterion via fuzzy set

Recently, many algorithms based on locally graph embedding are proposed for dimensional reduction in nonlinear data. However, these algorithms are not effective when dealing with face images affected by variations in illumination conditions, poses or perspectives and different facial expressions. So, distant data points are not deemphasized efficiently by locally graph embedding algorithms and it may degrade the performance of classification. In order to solve the aforementioned problem, this paper proposes a new efficient dimension reduction method-local graph embedding method based on maximum margin criterion via Fuzzy Set for face recognition. Firstly, the goal of this algorithm is preserved under nearest neighbor premise by constructing the fuzzy intrinsic graph and the fuzzy penalty graph. Secondly, two novel fuzzy Laplacian scatter matrices are calculated using Fuzzy K-Nearest Neighbor (FKNN) in the proposed method. Finally, Maximum Margin Criterion (MMC) is used to avoid the “small size sample” problem. The results of face recognition experiments on the ORL, YALE and AR face databases demonstrate the effectiveness of our proposed method.