Downsampling sparse representation and discriminant information aided occluded face recognition

In this paper, a strategy is proposed to deal with a challenging research topic, occluded face recog- nition. Our approach relies on sparse representation on downsampled input image to first locate unoccluded face parts, and then exploits the linear discriminant ability of those pixels to identify t...

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Bibliographic Details
Published in:Science China. Information sciences 2014-02, Vol.57 (3), p.134-141
Main Authors: Li, YueLong, Meng, Li, Feng, JuFu, Wu, JiGang
Format: Article
Language:English
Subjects:
Algorithms
Blocking
China
Computer Science
Face recognition
Image detection
Information Systems and Communication Service
Occlusion
Representations
Research Paper
Strategy
下采样
信息
稀疏表示
线性判别
脸部识别
训练样本
资助
遮挡检测
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Summary:In this paper, a strategy is proposed to deal with a challenging research topic, occluded face recog- nition. Our approach relies on sparse representation on downsampled input image to first locate unoccluded face parts, and then exploits the linear discriminant ability of those pixels to identify the input subject. The advantages and novelties of our method include, 1） since the sparse representation based occlusion detection is conducted on dowsampled image, our algorithm is much faster than classic SRC; 2） the discriminant informa- tion learned from training samples is combined with sparse representation to recognize occluded face for the first time. The verification experiments are conducted on both sinmlated block occlusion images and genuine occluded images.
ISSN:1674-733X
1869-1919
DOI:10.1007/s11432-013-4856-z