Maximum likelihood face detection

Author

Colmenarez, Antonio J. ; Huang, Thomas S.

Author_Institution

Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA

fYear

1996

fDate

14-16 Oct 1996

Firstpage

307

Lastpage

311

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/AFGR.1996.557282

Filename

557282