Masked SIFT with align‐based refinement for contactless palmprint recognition

Abstract

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-ofart methods on the same databases by 1.9% for verification and 3.2% for identification.

Democratic voting downsampling for coding‐based palmprint recognition

“…Feature extraction directly affects the discriminative ability of the palmprint recognition. In recent years, different features are extracted with several state‐of‐the‐art methods, such as subspace features [11–13], statistical features [14–16], geometrical features [17, 18]. When the feature size is large, effective dimensionality reduction technologies, including subspace technologies, can reduce the storage cost and computational complexity of matching.…”