Discriminative Super-Resolution method for Low-Resolution ear recognition

Abstract

The available images of biometrics recognition system in real-world applications are often degraded and of low-resolution, making the acquired images contain less detail information. Therefore, biometrics recognition of the low-resolution image is a challenging problem. It has received increasing attention in recent years. In this paper, a two-step ear recognition scheme based on super-resolution is proposed, which will contribute to both human-based and machine-based recognition. Unlike most standard super-resolution methods which aim to improve the visual quality of ordinary images, the proposed superresolution based method is designed to improve the recognition performance of low-resolution ear image, which uses LC-KSVD algorithm to learn much more discriminative atoms of the dictionary. When applied to low-resolution ear recognition problem, the proposed method achieves better recognition performance compared with the present super-resolution method.