Bayesian face detection in an image sequence using face probability gradient ascent

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.

• Type: Conference
• Year: 2005
• Where: ICIP
• Authors: Jae Hee Park, Hae-Chul Choi, Seong-Dae Kim
• Keywords: Bayesian Face Detection, ICIP 2005, Image Processing