Information theory and face detection
- 1 January 1996
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 3, 601-605 vol.3
- https://doi.org/10.1109/icpr.1996.547017
Abstract
Face detection in complex environments is an unsolved problem which has fundamental importance to face recognition, model based video coding, content based image retrieval, and human computer interaction. In this paper we model the face detection problem using information theory, and formulate information based measures for detecting faces by maximizing the feature class separation. The underlying principle is that search through an image can be viewed as a reduction of uncertainty in the classification of the image. The face detection algorithm is empirically compared using multiple test sets, which include four face databases from three universities.Keywords
This publication has 9 references indexed in Scilit:
- Human and machine recognition of faces: a surveyProceedings of the IEEE, 1995
- Learning human face detection in cluttered scenesPublished by Springer Nature ,1995
- Human face detection in a complex backgroundPattern Recognition, 1994
- View-based and modular eigenspaces for face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1994
- Feature extraction from faces using deformable templatesInternational Journal of Computer Vision, 1992
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1984
- Modeling by shortest data descriptionAutomatica, 1978
- Application of the Karhunen-Loève Expansion to Feature Selection and OrderingIEEE Transactions on Computers, 1970
- A Mathematical Theory of CommunicationBell System Technical Journal, 1948