Local Multiple Directional Pattern of Palmprint Image
This work addresses a domain-specific problem in biometrics by improving palmprint recognition accuracy, though it appears incremental as it builds on existing direction-based methods.
The paper tackles the problem of palmprint recognition by proposing a Local Multiple Directional Pattern (LMDP) to characterize multiple dominant directions in palmprint images, where conventional methods only capture a single direction. Experimental results show LMDP outperforms existing descriptors and state-of-the-art direction-based methods.
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.