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www.face-rec.org
GENERAL INFO Over the last ten years or so, face recognition has become a popular area of research in computer vision and one of the most successful applications of image analysis and understanding. Because of the nature of the problem, not only computer science researchers are interested in it, but neuroscientists and psychologists also. It is the general opinion that advances in computer vision
General Papers Here are some excellent papers that every researcher in this area should read. They present a logical introductory material into the field and describe latest achievements as well as currently unsolved issues of face recognition. W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, Face Recognition: A Literature Survey, ACM Computing Surveys, 2003, pp. 399-458 download here, 3.88 MB
DATABASES When benchmarking an algorithm it is recommendable to use a standard test data set for researchers to be able to directly compare the results. While there are many databases in use currently, the choice of an appropriate database to be used should be made based on the task given (aging, expressions, lighting etc). Another way is to choose the data set specific to the property to be teste
Image-Based Face Recognition Algorithms PCA|ICA|LDA|EP|EBGM|Kernel Methods|Trace Transform AAM|3-D Morphable Model|3-D Face Recognition Bayesian Framework|SVM|HMM|Boosting & Ensemble Algorithms Comparisons PCA Derived from Karhunen-Loeve's transformation. Given an s-dimensional vector representation of each face in a training set of images, Principal Component Analysis (PCA) tends to find a t-dime
Face Recognition Homepage / Relevant information in the the area of face recognition / Information pool for the face recognition community / Entry point for novices as well as a centralized information resource
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