VECTOR QUANTIZATION WITH CONSTRAINED LIKELIHOOD FOR FACE RECOGNITION

被引：0
|
作者：
Kostadinov, Dimche [1 ]
Voloshynovskiy, Sviatoslav [1 ]
Diephuis, Maurits [1 ]
Ferdowsi, Sohrab [1 ]
机构：
[1] Univ Geneva, Stochast Informat Proc Grp, Dept Comp Sci, 7 Route Drize, Geneva, Switzerland
来源：
2015 23RD EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2015年
关键词：
quantization; visual information encoding/decoding; face recognition; identification; SPARSE REPRESENTATION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we investigate the problem of visual information encoding and decoding for face recognition. We propose a decomposition representation with vector quantization and constrained likelihood projection. The optimal solution is considered from the point of view of the best achievable classification accuracy by minimizing the probability of error under a given class of distortions. The performance of the proposed model of information encoding/decoding is compared with the performance of those based on sparse representation. The computer simulation results confirm the superiority of the proposed vector quantization based recognition over sparse representation based recognition on several face image databases.
引用
收藏
页码：140 / 144
页数：5
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