Discriminative Super-Resolution method for Low-Resolution ear recognition

被引：0
作者：
机构：
[1] School of Automatic, University of Science and Technology Beijing, Beijing
来源：
Luo, Shuang | 1600年 / Springer Verlag卷 / 8833期
关键词：
Ear recognition; Label constraint dictionary learning; Low-resolution; Sparse coding; Super-resolution;
D O I：
10.1007/978-3-319-12484-1_50
中图分类号：
学科分类号：
摘要：
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. © Springer International Publishing Switzerland 2014.
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页码：422 / 450
页数：28
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