Maximum–minimum–median average MSD-based approach for face recognition

Abstract A new and efficient improved maximum scatter difference (MSD) model is introduced in this paper. The main weakness of the MSD model is that the class mean vector is constructed via class sample average when the within-class and between-class scatter matrices are formed. For a few of given samples with non-ideal conditions (e.g., variations of expression, pose and noisy environment), the assessment result is very weak by using the class sample average. That is because there will be some outliers in these samples. Therefore, the recognition performance of maximum scatter difference criterion will decline significantly. To solve the problem, in the traditional MSD model, we use within-class maximum–minimum–median average vector to construct within-class scatter matrix ( S w ) and between-class scatter matrix ( S b ) instead of within-class mean vector. The experimental results show that an improvement of the MSD model is possible with the proposed technique in ORL and Yale face database recognition problems.