Face Recognition Based on a 2D Gabor-Modular Binaryzation-LDA Feature Extraction Method
In this chapter, a novel feature extraction method (Gabor-modular binaryzation linear discriminant analysis (GMBLDA)) for face recognition is presented. 2D Gabor wavelet feature extraction is more robust for illumination and facial expression, however, the dimension of Gabor wavelet is very high and the redundancy rate becomes larger when it is used to feature extracting. For heavy work calculating, the poor real-time, and some other problems, this chapter proposes a way of integrating 2D Gabor-LDA with blocking and binaryzation. Transform face images to 2D Gabor wavelet at first and then use the blocking and binaryzation to reduce the dimensions and finally use LDA transformation to obtain the optimal classification characteristics. Experiment results based on YALE face database demonstrate that this method can achieve a higher recognition rate and a better recognition effect compared with the traditional way such as Gabor, principal component analysis (PCA), LDA, and Gabor + LDA (GLDA).
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