A New Face Recognition Approach to Boosting the Worst-Case Performance

In this paper, we aim to develop a new classifier for increasing the worst case performance for individual person. Technically, we adopt the idea from LDA and improve the worst recognition performance for individuals. This is achieved via introducing different weighting coefficients in LDA optimization process for obtaining the projection matrix. By increasing the weighting coefficients associated with the smallest between-class distance, the pair of classes with the nearest distance can exert more powerful influence on the optimization process in derivation of the projection matrix. The algorithm is tested on the Extended YaleB dataset and the ORL dataset.