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基于神經(jīng)網(wǎng)絡的人臉自動識別

於東軍,  徐蔚鴻,  趙海濤,  楊靜宇

於東軍, 徐蔚鴻, 趙海濤, 楊靜宇. 基于神經(jīng)網(wǎng)絡的人臉自動識別[J]. 電子與信息學報, 2003, 25(9): 1160-1167.
引用本文: 於東軍, 徐蔚鴻, 趙海濤, 楊靜宇. 基于神經(jīng)網(wǎng)絡的人臉自動識別[J]. 電子與信息學報, 2003, 25(9): 1160-1167.
Yu Dongjun, Xu Weihong, Zhao Haitao, Yang Jingyu. Automatic face recognition based on neural networks[J]. Journal of Electronics & Information Technology, 2003, 25(9): 1160-1167.
Citation: Yu Dongjun, Xu Weihong, Zhao Haitao, Yang Jingyu. Automatic face recognition based on neural networks[J]. Journal of Electronics & Information Technology, 2003, 25(9): 1160-1167.

基于神經(jīng)網(wǎng)絡的人臉自動識別

Automatic face recognition based on neural networks

  • 摘要: 該文應用神經(jīng)網(wǎng)絡技術構建了一個完整的人臉識別系統(tǒng)。首先使用基于眼睛位置估計的方法從人臉圖像中分割出對識別有意義的純臉,然后使用自組織映射進行特征壓縮,提取有效的鑒別特征。最后使用基于知識的模糊神經(jīng)網(wǎng)絡進行分類。在NUSTDBII人臉數(shù)據(jù)庫上的實驗得到了令人滿意的結(jié)果,并且表明該方法對人臉的姿態(tài)及表情有一定的不敏感性。
    Abstract: A complete face recognition system is built based on neural network technologies. Firstly, pure faces are extracted from original face images. Secondly, SOM is utilized to obtain feature reduction. Finally, knowledge-based fuzzy neural networks are used for classification. Experiments on NUSTDBII face database demonstrate the effectiveness of the proposed method and show that it does not sensitive to the variance of pose and expression to a certain extent.
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  • R. Chellappa, C. L.Wilson, S. Sirohey, Human and machine recognition of faces: a survey, Proc.IEEE, 1995, 83(5), 705 740.[2]A. Rosenfeld, Survey: Image analysis and computer vision: 1996, Computer Vision and Image Understanding, 1997, 66(1), 33-93.[3]S.H. Lin, S. Y. Kung, L. J. Lin, Face recognition/detection by probabilistic decision-based neural network, IEEE Trans. on Neural Networks, 1997, 8(1), 114-132.[4]Li-Fen Chen, et al., Why recognition in a statistics-based face recognition system should be based on the pure face portion: a probabilistic decision-based proof, Pattern Recognition, 2001, 34(1),1393-1403.[5]S. Lawrence, C. L. Giles, A. C. Tsoi, A. D. Back, Face recognition: a convolutional neural-network approach, IEEE Trans. on Neural Networks, 1997, 8(1), 98-113.[6]K.S. Yoon, et al., Hybrid approaches to frontal view face recognition using the hidden Markov model and neural network, Pattern Recognition, 1998, 31(3), 283-293.[7]A.J. Schofield, et al., A system for counting people in video images using neural networks to identify the background scene, Pattern Recognition, 1996, 29(8), 1425-1428.[8]N. Intrator, et al., Face recognition using a hybrid supervised/unsupervised neural network, Pattern Recognition Letters, 1996, 17(1), 67-76.[9]金忠,人臉圖像特征抽取與維數(shù)研究,[博士論文],南京,南京理工大學,1999.[10]T. Kohonen, The self-organizing map, Proc. IEEE, 1990, 78 (1), 1464-1480.[11]R. Babuska, H. B. Verbruggen, Fuzzy set methods for local modeling and identification, in Multiple Model Approaches to Nonlinear Modeling and Control, R. Murray-Smith and T. A. Johansen,Eds. London, U.K., Taylor and Francis, 1996.[12]Magne Setnes, Similarity measures in fuzzy rule base simplification, IEEE Trans. on System,Man and Cybernetics part B: Cybernetics, 1998, 28(3), 376-386.[13]Guodong Guo, Stan Z. Li, Kap Luk Chan, Support vector machines for face recognition, Image and Vision Computing, 2001, 19(2), 631-638.
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  • 文章訪問數(shù):  2270
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出版歷程
  • 收稿日期:  2002-04-09
  • 修回日期:  2002-09-05
  • 刊出日期:  2003-09-19

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