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Masked SIFT with align-based refinement for contactless palmprint recognition

Masked SIFT with align-based refinement for contactless palmprint recognition

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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 a contactless environment. For example, pose and illumination variations affect the layout and visibility of palm lines. This study 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 is then described by multi-descriptors rather than a single one. Second, only query and target keypoints with small rotation difference are compared together, instead of comparing them all. This speed-up the comparison and enhance the accuracy, versus 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 the same databases by 1.9% for verification and 3.2% for identification.

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