A general discriminant model for color face recognition

Jian Yang, Chengjun Liu

Research output: Contribution to conferencePaperpeer-review

36 Scopus citations


This paper presents a General Discriminant Model (GDM) for color face recognition. The GDM model involves two sets of variables: a set of color component combination coefficients for color image representation and a set of projection basis vectors for image discrimination. An iterative whitening-maximization (IWM) algorithm is designed to find the optimal solution of the model. The proposed algorithm is further extended to generate three color components (like the three color components of RGB color images) for further improving the face recognition performance. Experiments using the Face Recognition Grand Challenge (FRGC) database and the Biometric Experimentation Environment (BEE) system show the effectiveness of the proposed model and algorithm. In particular, for the most challenging FRGC version 2 Experiment 4, which contains 12,776 training images, 16,028 controlled target images, and 8,014 uncontrolled query images, the proposed method achieves the face verification rate (ROC III) of 74.91% at the false accept rate of 0.1% .

Original languageEnglish (US)
StatePublished - 2007
Event2007 IEEE 11th International Conference on Computer Vision, ICCV - Rio de Janeiro, Brazil
Duration: Oct 14 2007Oct 21 2007


Other2007 IEEE 11th International Conference on Computer Vision, ICCV
CityRio de Janeiro

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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