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Machine Learning - The Eleventh Annual Conference on computational Learning Theory
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FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
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IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subspace Analysis Using Random Mixture Models
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Fusion of face and speech data for person identity verification
IEEE Transactions on Neural Networks
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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 (EnL–DFLD) is proposed to improve the current FLD based face recognition algorithms. Firstly, the classifier ensemble in EnL–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 EnL–DFLD. Experiments on two common face databases verify the superiority of the proposed EnL–DFLD over the state-of-the-art algorithms in recognition accuracy.