Face detection in static images using Bayesian discriminating feature and particle attractive genetic algorithm
Hyun‐Chul Choi, Se‐Young Oh · 2005
This paper proposes a fast face detection technique which can find exact face regions in both gray and color static images using the Bayesian discriminating feature and the particle attractive genetic algorithm. In Bayesian discriminating feature method, face and nonface probability can be calculated with probabilistic models of the face and nonface feature vectors which consists of horizontal, vertical histograms, and 1D wavelets of the image inside the candidate window. These probabilities are modeled as Gaussian distribution and can be used to distinguish face regions from nonface regions. To search proper face candidate regions, we propose the particle attractive genetic algorithm which can fast converge on the exact face region. The proposed method demonstrates fast and precise detection results with the images obtained from offices or outdoor environments.