Incremental face recognition for large-scale social network services

被引:75
作者
Choi, Kwontaeg [1 ]
Toh, Kar-Ann [2 ]
Byun, Hyeran [1 ]
机构
[1] Yonsei Univ, Dept Comp Sci, Seoul 120749, South Korea
[2] Yonsei Univ, Sch Elect & Elect Engn, Seoul 120749, South Korea
基金
新加坡国家研究基金会;
关键词
Face recognition; Social network service; Incremental learning; Gabor filter; Neural network; LINEAR DISCRIMINANT-ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; GABOR WAVELETS; CLASSIFICATION; ALGORITHM; MACHINE;
D O I
10.1016/j.patcog.2012.02.002
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Due to the rapid growth of social network services such as Facebook and Twitter, incorporation of face recognition in these large-scale web services is attracting much attention in both academia and industry. The major problem in such applications is to deal efficiently with the growing number of samples as well as local appearance variations caused by diverse environments for the millions of users over time. In this paper, we focus on developing an incremental face recognition method for Twitter application. Particularly, a data-independent feature extraction method is proposed via binarization of a Gabor filter. Subsequently, the dimension of our Gabor representation is reduced considering various orientations at different grid positions. Finally, an incremental neural network is applied to learn the reduced Gabor features. We apply our method to a novel application which notifies new photograph uploading to related users without having their ID being identified. Our extensive experiments show that the proposed algorithm significantly outperforms several incremental face recognition methods with a dramatic reduction in computational speed. This shows the suitability of the proposed method for a large-scale web service with millions of users. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2868 / 2883
页数:16
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