Median MSD-based method for face recognition

An improved maximum scatter difference (MSD) criterion is proposed in this paper. A weakness of existing MSD model is that the class mean vector in the expressions of within-class scatter matrix and between-class scatter matrix is estimated by class sample average. Under the non-ideal conditions such as variations of expression, illumination, pose, and so on, there will be some outliers in the sample set, so the class sample average is not sufficient to provide an accurate estimate of the class mean using a few of given samples. As a result, the recognition performance of traditional MSD model will decrease. To address this problem, also to render MSD model rather robust, within-class median vector rather than within-class mean vector is used in the original MSD method. The results of experiments conducted on CAS-PEAL and FERET face database indicate the effectiveness of the proposed approach.