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光照变化条件下的人脸识别研究 被引量:6

Face Recognition under Varying Illumination
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摘要 本文提出了两种研究光照变化条件下人脸识别的方法。第1种方法是光照子空间方法,它适用于训练集中存在与测试人脸图像相同或者相似光照的人脸训练图像。当这个条件不满足时,可利用径向基函数网络产生虚拟光照条件下的人脸图像样本或图像特征加入训练集,该方法适用于更一般的情况。实验结果证明文中提出的方法可以有效提高识别率。 For tackling the problem of face recognition when illumination varied in direction, we proposed two face recognition algorithms. The first is illumination subspace method. We constructed different subspaces that correspond respectively to different illumination directions. We projected the test face image to the subspace having the same illumination direction and perform feature extraction. We then completed face recognition through feature matching between test image and the corresponding subspace. When applicable, illumination subspace method is quite effective. The second method is more general than the first. In the second method, we produced face images under virtual illumination, which is made possible through training RBFN (radial basis function network) with images whose illumination directions are known. Thus we can implement feature matching between test images under any illumination direction and produce virtual image having the same illumination direction. Experimental results show that the illumination subspace method achieves a recognition ratio higher than that achieved by the standard eigenface method. Experimental results also show that the produced face images under virtual illumination can be used effectively as training images without adversely affecting the recognition ratio.
出处 《西北工业大学学报》 EI CAS CSCD 北大核心 2004年第4期426-430,共5页 Journal of Northwestern Polytechnical University
基金 国家自然科学基金 (60 1 41 0 0 2 )资助
关键词 光照子空间 虚拟光照 特征脸 径向基函数网络 Feature extraction Illuminating engineering Image processing Radial basis function networks Standards
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参考文献5

  • 1[1]Pentland Alex, Moghaddam Baback, Starner Thad. View-Based and Modular Eigenspaces for Face Recognition. Tech-nical Report No.245, MIT Media Laboratory, 1994
  • 2[2]ftp://eris.wisdom.weizmann.ac.il/pub/FaceBase/. Shimon Edleman's Face Database
  • 3苏宏涛,赵荣椿.基于不同光照方向的子空间人脸识别方法[J].计算机工程,2003,29(8):124-125. 被引量:6
  • 4[4]Graham D B, Allinson N M. Automatic Face Representation and Classification. the 9th British Machine Vision Confe-rence. Shimon Edlemans face darabase, 1998: 64~73
  • 5[5]Howell A Jonathan, Buxton Hilary . Learning Identity with Radial Basis Function Networks. Neurocomputing, 1998, 20(1-3): 15~34

二级参考文献1

  • 1Pentland A, Moghaddam B,Starner T,View-based and Modular Eigenspaces for Face Recognition.Technical Report No.245,MIT Media Laboratory, 1994.

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