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Conference Paper
Bayesian face detection in an image sequence using face probability gradient ascent
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- Authors
- Jae Hee Park, Hae Chul Choi, Seong Dae Kim
- Issue Date
- 2005-09
- Citation
- International Conference on Image Processing (ICIP) 2005, pp.1-4
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Face detection in an image sequence is a challenging problem for many applications. In this paper, a novel face detection method is proposed. In order to detect faces in a sequence, based on Bayesian decision theory, we construct a unified framework of most face-like region selection, face/non-face classification, and detection result correction. And we propose Face Probability Gradient Ascent method to estimate the optimal position, scale, and rotation parameters of each face. In the experimental results, it is shown that the proposed method is more accurate and efficient than other conventional detection methods. © 2005 IEEE.
- KSP Keywords
- Bayesian decision theory, Detection Method, Face detection, Gradient Ascent Method, Image sequence, Optimal position, face classification, region selection, unified framework