Improved Anchored Neighborhood Regression Enhancement for Face Recognition

被引：0

作者：

王云飞 ^{[1
,2
]}

丁辉 ^{[1
,3
]}

尚媛园 ^{[1
,3
]}

邵珠宏 ^{[3
,4
]}

付小雁 ^{[3
,4
]}

机构：

[1] Beijing Advanced Innovation Center for Imaging Technology,Capital Normal University

[2] Department of Physics,Capital Normal University

[3] College of Information Engineering,Capital Normal University

[4] Beijing Key Laboratory of Electronic System Reliability Technology,Capital Normal University

来源：

Journal of Shanghai Jiaotong University(Science) | 2018年 / 23卷 / 05期

基金：

中国国家自然科学基金;

关键词：

enhancement; anchored neighborhood regression(ANR); recognition accuracy; feature evaluation operator;

D O I：

暂无

中图分类号：

TP391.41 [];

学科分类号：

080203 ;

摘要：

Although progress in face recognition is encouraging, the accuracy rate of face recognition remains to be increased. Since the face image quality has a positive influence on face recognition accuracy, the image enhancement methods are popular in face recognition. Most current image enhancement methods aim at improving visual appearance, but cannot improve recognition accuracy remarkably. In this paper, a feature evaluation operator is designed to overcome this problem. The operator selects patches with the best quality, and then face image is reconstructed with the selected patches. The proposed algorithm is tested on two different face recognition applications. Accuracy is raised after enhancement, and the result proves that the proposed algorithm is effective.

引用

页码：600 / 606

页数：7

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