Palmprint Verification for Images Captured in Peg-Less Scenarios

This paper presents a reliable and robust palmprint verification approach using palmprint feature point number (FPN). The feature verified by support vector machine (SVM). It has the advantages of capturing palm images in peg-less scenarios and by a low cost and low-resolution (100dpi) digital scanner. The low-resolution images lead a less database size. There are 4800 palmprint images were collected from 160 persons to verify the validity of the proposed approach and the results are satisfactory with 98.30% classification correct rate (CCR). Experimental results demonstrate that the proposed approach is feasible and effective in palmprint verification. Our findings will help to extend palmprint verification technologies to security access control systems.