@conference {14834,
	title = {Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on},
	year = {2011},
	month = {2011/06//},
	pages = {593 - 600},
	abstract = {This paper presents a novel way to perform multi-modal face recognition. We use Partial Least Squares (PLS) to linearly map images in different modalities to a common linear subspace in which they are highly correlated. PLS has been previously used effectively for feature selection in face recognition. We show both theoretically and experimentally that PLS can be used effectively across modalities. We also formulate a generic intermediate subspace comparison framework for multi-modal recognition. Surprisingly, we achieve high performance using only pixel intensities as features. We experimentally demonstrate the highest published recognition rates on the pose variations in the PIE data set, and also show that PLS can be used to compare sketches to photos, and to compare images taken at different resolutions.},
	keywords = {approximations;pose, COMPARISON, estimation;, extraction;image, framework;face, intensities;subspace, least, PLS;bypassing, recognition;feature, recognition;partial, resolution;least, selection;multimodal, squares, squares;pixel, synthesis;face},
	doi = {10.1109/CVPR.2011.5995350},
	author = {Sharma,A. and Jacobs, David W.}
}
@conference {13070,
	title = {A novel feature descriptor based on the shearlet transform},
	booktitle = {Image Processing (ICIP), 2011 18th IEEE International Conference on},
	year = {2011},
	month = {2011/09//},
	pages = {1033 - 1036},
	abstract = {Problems such as image classification, object detection and recognition rely on low-level feature descriptors to represent visual information. Several feature extraction methods have been proposed, including the Histograms of Oriented Gradients (HOG), which captures edge information by analyzing the distribution of intensity gradients and their directions. In addition to directions, the analysis of edge at different scales provides valuable information. Shearlet transforms provide a general framework for analyzing and representing data with anisotropic information at multiple scales. As a consequence, signal singularities, such as edges, can be precisely detected and located in images. Based on the idea of employing histograms to estimate the distribution of edge orientations and on the accurate multi-scale analysis provided by shearlet transforms, we propose a feature descriptor called Histograms of Shearlet Coefficients (HSC). Experimental results comparing HOG with HSC show that HSC provides significantly better results for the problems of texture classification and face identification.},
	keywords = {analysis;multiscale, analysis;object, classification;face, classification;image, classification;intensity, coefficients;image, descriptor;feature, detection;object, distribution, edge, EXTRACTION, extraction;image, gradient, gradients;histograms, identification;feature, methods;histograms, of, orientations;face, oriented, recognition;feature, recognition;shearlet, recognition;transforms;, shearlet, singularities;texture, texture;object, transform;signal},
	doi = {10.1109/ICIP.2011.6115600},
	author = {Schwartz, W.R. and da Silva,R.D. and Davis, Larry S. and Pedrini,H.}
}
@conference {12472,
	title = {Evaluation of state-of-the-art algorithms for remote face recognition},
	booktitle = {Image Processing (ICIP), 2010 17th IEEE International Conference on},
	year = {2010},
	month = {2010/09//},
	pages = {1581 - 1584},
	abstract = {In this paper, we describe a remote face database which has been acquired in an unconstrained outdoor environment. The face images in this database suffer from variations due to blur, poor illumination, pose, and occlusion. It is well known that many state-of-the-art still image-based face recognition algorithms work well, when constrained (frontal, well illuminated, high-resolution, sharp, and complete) face images are presented. In this paper, we evaluate the effectiveness of a subset of existing still image-based face recognition algorithms for the remote face data set. We demonstrate that in addition to applying a good classification algorithm, consistent detection of faces with fewer false alarms and finding features that are robust to variations mentioned above are very important for remote face recognition. Also setting up a comprehensive metric to evaluate the quality of face images is necessary in order to reject images that are of low quality.},
	keywords = {alarm;feature, algorithm;still, classification;image, classification;visual, database;remote, databases;, extraction;hidden, extraction;image, Face, feature, image-based, image;false, quality;occlusion;remote, recognition;face, recognition;feature, recognition;state-of-the-art, removal;image},
	doi = {10.1109/ICIP.2010.5652608},
	author = {Ni,Jie and Chellapa, Rama}
}
@conference {13102,
	title = {Learning Discriminative Appearance-Based Models Using Partial Least Squares},
	booktitle = {Computer Graphics and Image Processing (SIBGRAPI), 2009 XXII Brazilian Symposium on},
	year = {2009},
	month = {2009/10//},
	pages = {322 - 329},
	abstract = {Appearance information is essential for applications such as tracking and people recognition. One of the main problems of using appearance-based discriminative models is the ambiguities among classes when the number of persons being considered increases. To reduce the amount of ambiguity, we propose the use of a rich set of feature descriptors based on color, textures and edges. Another issue regarding appearance modeling is the limited number of training samples available for each appearance. The discriminative models are created using a powerful statistical tool called partial least squares (PLS), responsible for weighting the features according to their discriminative power for each different appearance. The experimental results, based on appearance-based person recognition, demonstrate that the use of an enriched feature set analyzed by PLS reduces the ambiguity among different appearances and provides higher recognition rates when compared to other machine learning techniques.},
	keywords = {(artificial, analysis;learning, appearance, approximations;object, based, colour, descriptors;learning, discriminative, intelligence);least, learning, least, models;machine, person, recognition;, recognition;feature, squares, squares;image, techniques;partial},
	doi = {10.1109/SIBGRAPI.2009.42},
	author = {Schwartz, W.R. and Davis, Larry S.}
}
@article {13162,
	title = {Detection and analysis of hair},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {28},
	year = {2006},
	month = {2006/07//},
	pages = {1164 - 1169},
	abstract = {We develop computational models for measuring hair appearance for comparing different people. The models and methods developed have applications to person recognition and image indexing. An automatic hair detection algorithm is described and results reported. A multidimensional representation of hair appearance is presented and computational algorithms are described. Results on a data set of 524 subjects are reported. Identification of people using hair attributes is compared to eigenface-based recognition along with a joint, eigenface-hair-based identification},
	keywords = {analysis;hair, appearance;image, AUTOMATIC, detection;, detection;hair, extraction;image, hair, indexing;multidimensional, recognition;face, recognition;feature, representation;object, representation;person},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2006.139},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
