Digital Image Processing
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Improved-LDA based face recognition using both facial global and local information
Pattern Recognition Letters
Elastic shape-texture matching for human face recognition
Pattern Recognition
Face recognition using a fuzzy fisherface classifier
Pattern Recognition
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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Face recognition is an important issue in pattern recognition. Linear discriminant analysis (LDA) has been widely used in face recognition. However, the LDA-based face recognition methods usually encountered the small sample size (SSS) problem. The SSS problem occurs when the number of samples is far smaller than the dimensionality of the sample space. Therefore, this paper proposed a modified LDA (called block LDA) to divide the input image into several non-overlapping subimages of the same size, in order to increase the quantity of samples and reduce the dimensions of the sample space. In addition, to reduce the influence of illumination variations, face images were transferred to gradient image. Experimental results show that the proposed method indeed solves the SSS problem with a good recognition rate.