@conference {12446,
	title = {Illumination robust dictionary-based face recognition},
	booktitle = {2011 18th IEEE International Conference on Image Processing (ICIP)},
	year = {2011},
	month = {2011/09/11/14},
	pages = {777 - 780},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {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.},
	keywords = {albedo, approximation theory, classification, competitive face recognition algorithms, Databases, Dictionaries, Face, face recognition, face recognition method, filtering theory, human face recognition, illumination robust dictionary-based face recognition, illumination variation, image representation, learned dictionary, learning (artificial intelligence), lighting, lighting conditions, multiple images, nonstationary stochastic filter, publicly available databases, relighting, relighting approach, representation error, residual vectors, Robustness, simultaneous sparse approximations, simultaneous sparse signal representation, sparseness constraint, Training, varying illumination, vectors},
	isbn = {978-1-4577-1304-0},
	doi = {10.1109/ICIP.2011.6116670},
	author = {Patel, Vishal M. and Tao Wu and Biswas,S. and Phillips,P.J. and Chellapa, Rama}
}
@conference {12485,
	title = {Pose-robust albedo estimation from a single image},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on},
	year = {2010},
	month = {2010/06//},
	pages = {2683 - 2690},
	abstract = {We present a stochastic filtering approach to perform albedo estimation from a single non-frontal face image. Albedo estimation has far reaching applications in various computer vision tasks like illumination-insensitive matching, shape recovery, etc. We extend the formulation proposed in that assumes face in known pose and present an algorithm that can perform albedo estimation from a single image even when pose information is inaccurate. 3D pose of the input face image is obtained as a byproduct of the algorithm. The proposed approach utilizes class-specific statistics of faces to iteratively improve albedo and pose estimates. Illustrations and experimental results are provided to show the effectiveness of the approach. We highlight the usefulness of the method for the task of matching faces across variations in pose and illumination. The facial pose estimates obtained are also compared against ground truth.},
	keywords = {3D, albedo, estimation;, estimation;shape, Face, filtering;face, image;single, image;stochastic, information;pose-robust, matching;pose, nonfrontal, pose;class-specific, recognition;filtering, recovery;single, statistics;computer, theory;pose, vision;illumination-insensitive},
	doi = {10.1109/CVPR.2010.5539987},
	author = {Biswas,S. and Chellapa, Rama}
}
@conference {12718,
	title = {Rank constrained recognition under unknown illuminations},
	booktitle = {Analysis and Modeling of Faces and Gestures, 2003. AMFG 2003. IEEE International Workshop on},
	year = {2003},
	month = {2003/10//},
	pages = {11 - 18},
	abstract = {Recognition under illumination variations is a challenging problem. The key is to successfully separate the illumination source from the observed appearance. Once separated, what remains is invariant to illuminant and appropriate for recognition. Most current efforts employ a Lambertian reflectance model with varying albedo field ignoring both attached and cast shadows, but restrict themselves by using object-specific samples, which undesirably deprives them of recognizing new objects not in the training samples. Using rank constraints on the albedo and the surface normal, we accomplish illumination separation in a more general setting, e.g., with class-specific samples via a factorization approach. In addition, we handle shadows (both attached and cast ones) by treating them as missing values, and resolve the ambiguities in the factorization method by enforcing integrability. As far as recognition is concerned, a bootstrap set which is just a collection of two-dimensional image observations can be utilized to avoid the explicit requirement that three-dimensional information be available. Our approaches produce good recognition results as shown in our experiments using the PIE database.},
	keywords = {albedo, approach;, constrained, database;, databases;, decomposition;, factorization, field;, illumination, image;, information;, Lambertian, lighting;, model;, object, object-specific, PIE, rank, recognition;, reflectance, samples;, singular, three-dimensional, two-dimensional, value, variations;, visual},
	doi = {10.1109/AMFG.2003.1240818},
	author = {Zhou, S. and Chellapa, Rama}
}
