Illumination invariant face recognition based on dual-tree complex wavelet transform

This study presents a new dual-tree complex wavelet transform (DT-CWT)-based illumination normalisation approach for face recognition under varying lighting conditions. The method consists of three steps. First, the DT-CWT-based edge detection method is proposed which can obtain estimation for facial feature edges in different directionality and resolution level. Second, the DT-CWT-based denoising model is employed to obtain the multi-scale illumination invariant structures in the logarithm domain. Finally, by combining the obtained illumination invariant features and edge estimation information, the enhanced facial features are obtained which have more discriminating power for variable lighting face recognition. The effectiveness of the method is validated in comparative performance against many classical illumination compensation methods using the YaleB database and the CMU PIE database.