Fuzzy bidirectional maximum margin criterion based face recognition

This paper proposes a new method for feature extraction and recognition, namely, the fuzzy bidirectional maximum margin criterion (FBMMC). Through introducing the fuzzy membership grade matrix of the original training sample set, FBMMC defines the row directional fuzzy image scatter matrices and the row directional fuzzy image MMC, and then obtains the row directional optimal projection matrix. Subsequently, each sample in the original training sample set is transformed using the row directional optimal projection matrix, and the row directional feature training sample set can be obtained. Similarly, utilizing the fuzzy membership grade matrix of the row directional feature training sample set, FBMMC defines the formulas of the column directional fuzzy image scatter matrices and the column directional fuzzy image MMC; and then obtains the column directional optimal projection matrix. Having obtained the row and column directional optimal projection matrices, FBMMC can transform the original sample data from original high-dimensional data space to a low-dimensional feature space and complete the feature extraction of the original sample data. Experimental results on the ORL and Yale face database show that the proposed FBMMC method for face recognition has high recognition rate.