Coupling Adaboost and Random Subspace for Diversified Fisher Linear Discriminant

Fisher linear discriminant (FLD) is a popular method for feature extraction in face recognition. However, It often suffers from the small sample size, bias and overfitting problems when dealing with the high dimensional face image data. In this paper, a framework of ensemble learning for diversified Fisher linear discriminant (E_nL - DFLD) is proposed to improve the current FLD based face recognition algorithms. Firstly, the classifier ensemble in E_nL - DFLD is composed of a set of diversified component FLD classifiers, which are selected intentionally by computing the diversity between the candidate component classifiers. Secondly, the candidate component classifiers are constructed by coupling the random subspace and adaboost methods, and it can also be shown that such a coupling scheme will result in more suitable component classifiers so as to increase the generalization performance of E_nL - DFLD. Experiments on two common face databases verify the superiority of the proposed E_nL - DFLD over the state-of-the-art algorithms in recognition accuracy