Dictionary-Based Face Recognition Under Variable Lighting and Pose

• Published in: IEEE transactions on information forensics and security Vol. 7; no. 3; pp. 954 - 965
• Main Authors: Patel, V. M., Tao Wu, Biswas, S., Phillips, P. J., Chellappa, R.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-06-2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Albedo; Algorithms; Biometrics; Dictionaries; dictionary learning; Face recognition; Illumination; illumination variation; Lighting; outlier rejection; Robustness; Training
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2012.2189205

We present a face recognition algorithm based on simultaneous sparse approximations under varying illumination and pose. A dictionary is learned for each class based on given training examples which minimizes the representation error with a sparseness constraint. A novel test image is projected onto the span of the atoms in each learned dictionary. The resulting residual vectors are then used for classification. To handle variations in lighting conditions and pose, an image relighting technique based on pose-robust albedo estimation is used to generate multiple frontal images of the same person with variable lighting. As a result, the proposed algorithm has the ability to recognize human faces with high accuracy even when only a single or a very few images per person are provided for training. The efficiency of the proposed method is demonstrated using publicly available databases available databases and it is shown that this method is efficient and can perform significantly better than many competitive face recognition algorithms.