TY - CONF T1 - Illumination robust dictionary-based face recognition T2 - 2011 18th IEEE International Conference on Image Processing (ICIP) Y1 - 2011 A1 - Patel, Vishal M. A1 - Tao Wu A1 - Biswas,S. A1 - Phillips,P.J. A1 - Chellapa, Rama KW - albedo KW - approximation theory KW - classification KW - competitive face recognition algorithms KW - Databases KW - Dictionaries KW - Face KW - face recognition KW - face recognition method KW - filtering theory KW - human face recognition KW - illumination robust dictionary-based face recognition KW - illumination variation KW - image representation KW - learned dictionary KW - learning (artificial intelligence) KW - lighting KW - lighting conditions KW - multiple images KW - nonstationary stochastic filter KW - publicly available databases KW - relighting KW - relighting approach KW - representation error KW - residual vectors KW - Robustness KW - simultaneous sparse approximations KW - simultaneous sparse signal representation KW - sparseness constraint KW - Training KW - varying illumination KW - vectors AB - In this paper, we present a face recognition method based on simultaneous sparse approximations under varying illumination. Our method consists of two main stages. In the first stage, a dictionary is learned for each face class based on given training examples which minimizes the representation error with a sparseness constraint. In the second stage, a test image is projected onto the span of the atoms in each learned dictionary. The resulting residual vectors are then used for classification. Furthermore, to handle changes in lighting conditions, we use a relighting approach based on a non-stationary stochastic filter to generate multiple images of the same person with different lighting. As a result, our algorithm has the ability to recognize human faces with good accuracy even when only a single or a very few images are provided for training. The effectiveness of the proposed method is demonstrated on publicly available databases and it is shown that this method is efficient and can perform significantly better than many competitive face recognition algorithms. JA - 2011 18th IEEE International Conference on Image Processing (ICIP) PB - IEEE SN - 978-1-4577-1304-0 M3 - 10.1109/ICIP.2011.6116670 ER -