Region-based Quality Improvement of Facial Images with Strong Shadows to Enhance Recognition

Varying illuminations, especially the side lighting effects in face images, is one of the major obstacles in face recognition systems. Various methods have been presented for face recognition under different lighting conditions witch require previous knowledge about Light source and shadow area. In this paper, a novel approach based on H-minima transform to image segmentation and illumination normalization is proposed. Firstly, shadow area is extracted and modified by multi-stage method. Then, the gradient based criteria used to determine the best pattern of shadow areas. Subsequently, the obtained pattern of shadow areas is used to improve Retinex method in obscure area, obvious area, and all areas of the face image. Experimental results on the Extended Yale B database show that the proposed method significantly improves the performance of Retinex method. Furthermore, it provides the effective results in illumination normalization even in extreme lighting conditions.