In view of the non-linear change of face image caused by illumination, the recognition rate is reduced and the ability of single feature expression is limited. Based on local binary mode (LBP) and Gabor wavelet, we propose a face recognition method. By extracting these two local features which are robust to illumination, we use generalized discriminant analysis (GDA) to reduce the dimension and then use discriminant correlation analysis (DCA) to fuse the features. Experiments on the ORL, Extended YaleB, OFD, and CAS-PEAL face databases demonstrate that the proposed method works better than LBP or Gabor alone.
Paper
Full text
Face Recognition in Complex Lighting Environment
Semantic Scholar · Computer Science · 2019
Abstract
In view of the non-linear change of face image caused by illumination, the recognition rate is reduced and the ability of single feature expression is limited. Based on local binary mode (LBP) and Gabor wavelet, we propose a face recognition method. By extracting these two local features which are robust to illumination, we use generalized discriminant analysis (GDA) to reduce the dimension and then use discriminant correlation analysis (DCA) to fuse the features. Experiments on the ORL, Extended YaleB, OFD, and CAS-PEAL face databases demonstrate that the proposed method works better than LBP or Gabor alone.