Discriminative FaceTopics for Face Recognition via Latent Dirichlet Allocation

被引:0
作者
Dhamecha, Tejas Indulal [1 ]
Sharma, Praneet [1 ]
Singh, Richa [1 ]
Vatsa, Mayank [1 ]
机构
[1] IIIT Delhi, New Delhi, Delhi, India
来源
2016 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2016) | 2016年
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Latent Dirichlet Allocation is a widely used approach for topic modeling and it has been successfully applied in several information retrieval applications. In this paper, we introduce this modeling technique for face recognition, by making an analogy between the two domains. We utilize latent Dirichlet allocation to represent facial regions in terms of FaceTopics. Further, linear discriminant analysis is utilized to obtain discriminative FaceTopics which are more suitable for classification tasks. The performance of the proposed approach is evaluated on the CMU-MultiPIE dataset under illumination and expression variations. The evaluation on over more than 50k images shows the effectiveness of the proposed approach. Further, the proposed approach shows improved identification results on e-PRIP dataset for matching composite sketches to photos.
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页数:8
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