Title :
Maximum likelihood face detection
Author :
Colmenarez, Antonio J. ; Huang, Thomas S.
Author_Institution :
Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA
Abstract :
We present a visual learning approach that uses non-parametric probability estimators. We use entropy analysis over the training set in order to select the features that best represent the pattern class of faces, and set up discrete probability models. These models are tested in the context of maximum likelihood detection of faces. Excellent results are reported in terms of the correct-answer-false-alarm tradeoff as well as in terms of the computational requirements of the systems
Keywords :
edge detection; face recognition; feature extraction; learning (artificial intelligence); maximum likelihood detection; nonparametric statistics; probability; computational requirements; correct-answer-false-alarm tradeoff; discrete probability models; entropy analysis; maximum likelihood face detection; nonparametric probability estimators; pattern class; training set; visual learning; Context modeling; Entropy; Face detection; Machine vision; Maximum likelihood detection; Maximum likelihood estimation; Pattern recognition; Probability distribution; Robustness; Testing;
Conference_Titel :
Automatic Face and Gesture Recognition, 1996., Proceedings of the Second International Conference on
Conference_Location :
Killington, VT
Print_ISBN :
0-8186-7713-9
DOI :
10.1109/AFGR.1996.557282
