Modeling Self-Principal Component Analysis for Age Invariant Face Recognition

被引：0
作者：
Nayak, Jyothi S. [1 ]
Indiramma, M. [1 ]
Nagarathna, N. [1 ]
机构：
[1] BMS Coll Engn, Dept Comp Sci & Engn, Bangalore, Karnataka, India
来源：
2012 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH (ICCIC) | 2012年
关键词：
Age Invariant Face Recognition; Periocular Region; Self Eigen;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Face recognition is a biometric approach which can extract the facial characteristics of a person without his/her cooperation. The face recognition system fails to identify a person after some years because of the age related variations shown on the face. The face recognition system should be able to handle such age related variations and recognise the person irrespective of his age. The aging variations on a face are seen in the form of wrinkles, shape changes etc. The process of aging is highly composite and its effects on face are unpredictable. The aging effects are unique to every individual's face. In this paper we try to model the age variations of face using a self - Principal Component Analysis (PCA) based method. We have compared the recognition rate with the conventional PCA and an improved recognition rate is obtained using our method.
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页码：402 / 406
页数：5
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