Spatial Feature Interdependence Matrix (SFIM): A Robust Descriptor for Face Recognition

被引:0
|
作者
Yao, Anbang [1 ]
Yu, Shan [2 ]
机构
[1] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100090, Peoples R China
[2] National Inst Res Comp Sci & Control, Le Chesnay, France
来源
ADVANCES IN IMAGE AND VIDEO TECHNOLOGY, PT I | 2011年 / 7087卷
关键词
Face recognition; spatial feature interdependence matrix; spectral regression discriminant analysis; object representation; ORIENTED GRADIENTS; REGION COVARIANCE; REPRESENTATION; HISTOGRAMS; EIGENFACES; PATTERNS; PCA;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, a new face descriptor called spatial feature interdependence matrix (SFIM) is proposed for addressing representation of human faces under variations of illumination and facial expression. Unlike traditional face descriptors which usually use a hierarchically organized or a sequentially concatenated structure to describe the spatial arrangement of features in different facial regions, SFIM is focused on exploring inherent spatial feature interdependences among separated facial regions in a face image. We compute the feature interdependence strength between each pair of facial regions as the Chi square distance between two corresponding histogram based feature vectors. Once face images are represented as SFIMs, we then employ spectral regression discriminant analysis (SRDA) to achieve face recognition under a nearest neighbor search framework. Extensive experimental results on two well-known face databases demonstrate that the proposed method has superior performance in comparison with related approaches.
引用
收藏
页码:132 / +
页数:4
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