Masked SIFT with Align-Based Refinement for Contactless Palmprint Recognition

摘要： Contactless palmprint is considered more user convenient than other biometrics due to its acquisition simplicity and less-private nature. Many challenges arise which affect the performance of common contact-based methods when applied to contactless environment. For example, pose and illumination variations affect the layout and visibility of palm lines. This paper proposes a SIFT-based method with three main modifications from the traditional SIFT. First, the palm regions with no significant lines/wrinkles are masked out to reduce the false features. A region with multi lines are then described by multi descriptors rather than a single one. Second, instead of comparing all query keypoints with all target ones, only those with small rotation difference are matched together. This speed-up the comparison process and enhance the accuracy, compared with SIFT, by reducing the wrong matches. Third, an align-based refinement is applied to filter out the incorrect matches. The method is tested on three contactless hand databases; IITD, GPDS and Sfax-Miracl. It achieves a verification equal error rate of 0.72%, 0.84% and 1.14% and a correct identification rate of 98.9%, 99% and 98.9% on each database, respectively. These results are significantly better than the state-of-art methods on same databases by 1.9% for verification and 3.2% for identification.