A Fast and Robust Personal Identification Approach Using Handprint

Recently, handprint-based personal identification is widely being researched. Existing identification systems are nearly based on peg or peg-free stretched gray handprint images and most of them only using single feature to implement identification. In contrast to existing systems, color handprint images with incorporate gesture based on peg-free are captured and both hand shape features and palmprint texture features are used to facilitate coarse-to-fine dynamic identification. The wavelet zero-crossing method is first used to extract hand shape features to guide the fast selection of a small set of similar candidates from the database. Then, a modified LoG filter which is robust against brightness is proposed to extract the texture of palmprint. Finally, both global and local texture features of the ROI are extracted for determining the final output from the selected set of similar candidates. Experimental results show the superiority and effectiveness of the proposed approach.