Fuzzy Bidirectional Weighted Sum for Face Recognition

ABSTRACT

A new method for feature extraction and recognition, namely the fuzzy bidirectional weighted sum criterion (FBWSC) is proposed in this paper. FBWSC defines the row directional fuzzy image optimal image projection matrix. Subsequently, each sample in the original training sample set is transformed using the row directional optimal image projection matrix, and the row directional feature training sample set is obtained. Through the fuzzy distance, the row directional weight can be calculated. Similarly, FBWSC defines the column directional fuzzy image optimal image projection matrix; and then obtains the column directional feature training sample set. The column directional weight can be calculated using the fuzzy distance. Having obtained the row and column directional weight, FBWSC can sum the weight of row and column directional feature training sample sets, and then complete the feature extraction of the original sample data. Experiments on the ORL, FERET and Yale face database show that the proposed FBWSC method for face recognition has high recognition rate.

Keywords:

face recognition, feature extraction, fuzzy bidirectional, fuzzy distance, weighted sum criterion.