Face Detection Based on PSO and Neural Network
This paper presents a novel method to search face candidate regions in color images.First,a skin-color model is used to get the skin regions.Then,particle swarm optimization (PSO) is utilized for searching face candidate regions,which can save time and eliminate small noises.Experimental results show that this searching method is super to the conventional method which scans the whole image pixel by pixel.Finally,BP neural network is used to verify the face.The output error formula is modified to make the neural network converge more quickly.Bootstrap method is utilized to choose the training samples for the network,which reduces the correlation between samples and improves the detection effects.This approach is robust and can achieve high detection rate when detecting frontal faces,a little rotated and profile faces.In addition,it can detect faces with different size,expression,as well as having glasses and beard.
face detection skin-color model particle swarm optimization neural network
Yanjiang Wang Xiaoping Liu Peng Suo
College of Information and Control Engineering,China University of Petroleum,Dongying,Shandong 257061,P.R.China
国际会议
9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)
北京
英文
2008-10-26（万方平台首次上网日期，不代表论文的发表时间）