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This paper presents multipose faces recognition. The proposed scheme is based on holistic information of face image and small modification of classical LDA (modified LDA) classifier. The holistic information called as facial features is obtained by multiresolution wavelet analysis. The modified LDA (MLDA) classifier that works based on multivariate analysis classifies the facial features to a person’s class. The objectives of the proposed method are to create a compact and meaningful facial features without removing significant face image information, to build a simple classification technique which can well classify face images to a person’s class, to make the M-LDA-based training system to solve the retraining problem of the PCA and LDA based recognition system, to reduce the high memory space requirement of classical LDA and PCA, and to compare the effectiveness of proposed method to established LDA based recognition systems such as RLDA, DLDA, and SLDA. The result shows that the proposed method gives good enough performance i.e. high enough success rate, short time processing, and small enough EER compare to establish LDA. In addition, the wavelet transforms is an efficient way for reducing the dimensional size of original image.

Modified LDA classifier in multi resolution wavelet domain for multi-pose face recognition