Enhanced histogram of oriented lines for palmprint recognition

Abstract

Recently, two new emerging biometrics technologies, 2D low-resolution palmprint recognition technologies, have received wide attention. Numerous methods have been developed and proposed for palmprint recognition. Among them, one local descriptor, histogram of oriented lines (HOL), has achieved very desirable performance. However, HOL is constructed on a single scale and only exploits the orientation of the minimum response, and thus may lose some important information. In this paper, aiming to further increase the discriminative power of HOL, we propose an enhanced HOL (EHOL) descriptor jointly exploring the information in multiscale and multi-orientation maps for robust palmprint recognition. Furthermore, the EHOL descriptor is only constructed by orientation information and does not use histogram normalization and block strategies to generate the final histograms. Thorough experiments are conducted on two palmprint databases. The experimental results demonstrate that the recognition performance of the proposed EHOL is obviously better than that of state-of-the-art methods.