Face recognition using enhanced linear discriminant analysis
There are two fundamental problems with the linear discriminant analysis (LDA) for face recognition. First one is LDA is not stable because of the small training sample size problem. The other is that it would collapse the data samples of different classes into one single cluster when the class distributions are multimodal. An enhanced LDA method is proposed to overcome these two problems. The between- and within-class scatters are reformulated by introducing two different weighted matrices in respective. The enhanced Fisher criterion is then presented, which can preserve the local structure of different class in the reduced subspace. Moreover, maximum margin criterion is adopted to avoid the singularity problem of the within-class scatter matrix. Extensive experiments show encouraging recognition performance of the proposed algorithm.