High-speed face recognition based on discrete cosine transform and RBF neural networks

被引:152
作者
Er, MJ [1 ]
Chen, WL
Wu, SQ
机构
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Comp Control Lab, Singapore 639758, Singapore
[2] Inst Infocomm Res, Dept HumanCentr Media, Singapore 119613, Singapore
来源
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2005年 / 16卷 / 03期
关键词
discrete cosine transform (DCT); face recognition; FERET database; Fisher's linear discriminant (FLD); illumination invariance; Olivetti Research Laboratory (ORL) database; radial basis function (RBF) neural networks; Yale database;
D O I
10.1109/TNN.2005.844909
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, an efficient method for high-speed face recognition based on the discrete cosine transform (DCT), the Fisher's linear discriminant (FLD) and radial basis function (RBF) neural networks is presented. First, the dimensionality of the original face image is reduced by using the DCT and the large area illumination variations are alleviated by discarding the first few low-frequency DCT coefficients. Next, the truncated DCT coefficient vectors are clustered using the proposed clustering algorithm. This process makes the subsequent FLD more efficient. After implementing the FLD, the most discriminating an invariant facial features are maintained and the training samples are clustered well. As a consequence, further parameter estimation for the RBF neural networks is fulfilled easily which facilitates fast training in the RBF neural networks. Simulation results show that the proposed system achieves excellent performance with high training and recognition speed, high recognition rate as well as very good illumination robustness.
引用
收藏
页码:679 / 691
页数:13
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