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Palmprint recognition has attracted various researchers in recent years due to its richness in amount of features. In this paper, palmprint authentication system is classified into palmprint acquisition, preprocessing, feature extraction, feature fusion and matching. In the preprocessing stage the authors employed a modified preprocessing technique to extract the ROI and it is further enhanced using adaptive histogram equalization. In feature extraction, the single sample representation has become bottleneck in producing high performance. To solve this they propose an intramodal feature fusion for palmprint authentication. The proposed system extracts multiple features like Texture (Gabor), Line and Appearance (PCA) features from the preprocessed palmprint images.
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