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Illumination invariance for face verification

The task of face verification is made more difficult when the illumination conditions of image capture are not constrained. The differences in illumination conditions between the stored images of the client and the probe image can be lessened by the application of photometric normalisation. Photometric normalisation is the method of pre-processing an image to a representation that is robust to the illumination conditions of image capture. This thesis presents experiments comparing several photometric normalisation methods. The results demonstrate that the anisotropic smoothing pre-processing algorithm of Gross and Brajovic yields the best results of the photometric normalisations tested. The thesis presents an investigation into the behaviour of the anisotropic smoothing method, showing that performance is sensitive to the selection of its parameter. A method of optimising this parameter is suggested and experimental results show that it offers an improvement in verification rates. The variation of illumination across regions of the face is smaller than across the whole face. A novel component-based approach to face verification is presented to take advantage of this fact. The approach consists of carrying out verification on a number of images containing components of the face and fusing the result. As the component images are more robust to illumination, the choice of photometric normalisation is again investigated in the component-based context. The thesis presents the useful result that the simpler normalisations offer the best results when applied to facial component images. Experiments investigating the various methods of fusing the information from the components are presented, as is the issue of score normalisation. Methods of selecting which components are most useful for verification are also tested. The method of pruning the negative components of the linear discriminant analysis weight vector has been applied to the task of selecting the best subset of face components for verification. The pruned linear discriminant analysis method does not perform as well as the well known sequential floating forward selection method on the well illuminated XM2VTS database, however it achieves better generalisation when applied to the more challenging conditions of the XM2VTS dark set.