A review on Gabor wavelets for face recognition

被引:32
作者
Linlin Shen
Li Bai
机构
[1] University of Nottingham,School of Computer Science and Information Technology
来源
Pattern Analysis and Applications | 2006年 / 9卷
关键词
Joint time–frequency analysis; Gabor wavelets; Face recognition;
D O I
暂无
中图分类号
学科分类号
摘要
Due to the robustness of Gabor features against local distortions caused by variance of illumination, expression and pose, they have been successfully applied for face recognition. The Facial Recognition Technology (FERET) evaluation and the recent Face Verification Competition (FVC2004) have seen the top performance of Gabor feature based methods. This paper aims to give a detailed survey of state of the art 2D face recognition algorithms using Gabor wavelets for feature extraction. Existing problems are covered and possible solutions are suggested.
引用
收藏
页码:273 / 292
页数:19
相关论文
共 71 条
  • [1] Brunelli R(1993)Face recognition—features versus templates IEEE Trans PAMI 15 1042-1052
  • [2] Poggio T(1991)Eigenfaces for recognition J Cogn Neurosci 3 71-86
  • [3] Turk M(1997)Face recognition: the problem of compensating for changes in illumination direction IEEE Trans PAMI 19 721-732
  • [4] Pentland A(1936)The use of multiple measures in taxonomic problems Ann Eugen 7 179-188
  • [5] Adini Y(1997)Eigenfaces vs. Fisherfaces: recognition using class specific linear projection IEEE Trans PAMI 19 711-720
  • [6] Moses Y(2003)Regularized discriminant analysis for the small sample size problem in face recognition Pattern Recognit Lett 24 3079-3087
  • [7] Ullman S(2001)A direct LDA algorithm for high-dimensional data—with application to face recognition Pattern Recognit 34 2067-2070
  • [8] Fisher RA(2002)Face recognition with radial basis function (RBF) neural networks IEEE Trans Neural Netw 13 697-710
  • [9] Belhumeur PN(1995)Human and machine recognition of faces: a survey Proc IEEE 83 705-740
  • [10] Hespanha JP(1997)Comparing support vector machines with Gaussian kernels to radial basis function classifiers IEEE Trans Signal Process 45 2758-2765