DISCRIMINATIVE AND ROBUST COMPETITIVE CODE FOR PALMPRINT RECOGNITION

Abstract

Various palmprint recognition methods have been proposed based on orientation features of palmprints. Among them, the competitive code method using the dominant orientation of palmprint images achieves promising performance in palmprint recognition. In this paper, we propose a discriminative and robust competitive code based method, which uses a more accurate dominant orientation representation of palmprint images for palmprint authentication. Moreover, we propose to weight the orientation information of a neighbor area to improve the precision and stability of the discriminative and robust dominant orientation code. Experiments performed on three types of palmprint databases and a noisy dataset validate the effectiveness of the proposed method.

Existing System

Normalized 2-D Gabor filter, robust line orientation code (RLOC) method

Proposed System

In this paper, a discriminative and robust dominant orientation-based method is proposed, which extracts not only the dominant orientation code of palmprint images but also the side code of the dominant orientation code. Combining the dominant orientation code with the side code can accurately represent the most dominant orientation feature of palmprint images. Further, by weighting the convolution results in orientation extraction, the precision and stability of the dominant orientation feature can be effectively improved. In addition, the number of Gabor filters used in the method is the same as the conventional competitive code method, but it can obtain more accurate dominant orientation features of palmprint images. Extensive experiments performed on public palmprint databases and synthetic noisy dataset demonstrated that the proposed method outperforms the state-of-the-art orientation-based coding methods.

Architecture

BLOCK DIAGRAM