Dimensionality reduction based on minimax risk criterion for face recognition

In the field of pattern recognition and machine learning, many problems are involved in the tasks of dimensionality reduction and then classification. In this paper, we develop an efficient dimensionality reduction method named MiniRisk Supervised Discrimiant Projection (MRSDP), which extracts effective low-dimensional features for classification purpose. The proposed method utilizes discriminant information to guide the procedure of extracting intrinsic low-dimensional features and provides a linear projection matrix. Since MRSDP is based on minimax risk criterion, it can minimize the maximal probability of misclassification in the common borders of different classes of data by contracting within-class scatter and maximizing between-class scatter. The advantage of our method is borne out by comparison with other widely used methods. In the experiments on Yale face database and ORL face database, our method achieves constantly superior performance than those competing methods.