Recognition using extended multiple discriminant analysis (EMDA) method

In this paper, we propose an extended multiple discriminant analysis (EMDA) method to solve the feature extraction problem in high dimensional pattern space for multiple classes discrimination. The proposed method can be seen as an extension of the traditional multiple discriminant analysis (MDA) method. which requires that the within-class scatter matrix is nonsingular. However, we may face many discriminant problems that do not satisfy this condition, such as the face recognition where the within-class matrix is often singular. To solve this problem, we extend the traditional MDA method and propose the EMDA method. The feature extraction strategy of the EMDA method is to maximize the trace of the between-class scatter matrix while constrains the trace of the within-class scatter matrix to be zero. However, to find the solution of the EMDA turns out to be a difficult task. In this paper, we find a way to overcome this problem. With the ORL face database, experiments show that the EMDA method reaches the lowest average class error rate compared to the eigenface method and the Fisherface method, which is only 49.9% of that the traditional eigenface and 79.4% of that the Fisherface method.