Dual optimal multiband features for face recognition

Illumination and expression variations degrade the performance of a face recognition system. In this paper, a novel dual optimal multiband features method for face recognition is presented. This method aims to increase the robustness of face recognition system to both illumination and expression variations. The wavelet packet transform decomposes image into frequency subbands and the multiband feature fusion technique is incorporated to select optimal multiband feature sets that are invariant to illumination and expression variation separately. Parallel radial basis function neural networks are employed to classify the two sets of feature. The scores generated are then combined and processed by an adaptive fusion mechanism. In this mechanism, the level of illumination variations of the input image is estimated and the weights are assigned to the scores accordingly. Experiments based on Yale, YaleB, AR and ORL databases show that the proposed method outperformed other algorithms.