Local Multiple Directional Pattern of Palmprint Image

TL;DR

This paper introduces a local multiple directional pattern (LMDP) method that captures multiple dominant line directions in palmprint images, improving recognition accuracy over traditional single-direction methods.

Contribution

The paper proposes a novel LMDP technique that accurately characterizes multiple line directions and their confidence levels in palmprint images, enhancing recognition performance.

Findings

LMDP outperforms conventional descriptors in palmprint recognition.

LMDP effectively captures multiple dominant directions in crossing line regions.

Experimental results demonstrate the superiority of LMDP over state-of-the-art methods.

Abstract

Lines are the most essential and discriminative features of palmprint images, which motivate researches to propose various line direction based methods for palmprint recognition. Conventional methods usually capture the only one of the most dominant direction of palmprint images. However, a number of points in palmprint images have double or even more than two dominant directions because of a plenty of crossing lines of palmprint images. In this paper, we propose a local multiple directional pattern (LMDP) to effectively characterize the multiple direction features of palmprint images. LMDP can not only exactly denote the number and positions of dominant directions but also effectively reflect the confidence of each dominant direction. Then, a simple and effective coding scheme is designed to represent the LMDP and a block-wise LMDP descriptor is used as the feature space of palmprint images in palmprint recognition. Extensive experimental results demonstrate the superiority of the LMDP over the conventional powerful descriptors and the state-of-the-art direction based methods in palmprint recognition.