Face detection algorithm based on hybrid Monte Carlo method and Bayesian support vector machine. (5th July 2012)

• Record Type: Journal Article
• Authors: Wang, Liejun; Zhang, Taiyi; Jia, Zhenhong; Ding, Liang; Ren, Pinyi; Su, Zhou
• Is Part Of: Concurrency and computation. Volume 25:Number 9(2013:Jun.)
• Page Start: 1064
• Page End: 1072
• Publication Date: 2012-07-05
• Subjects: Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.2874
• Languages: English
• ISSNs: 1532-0626

Abstract

<abstract abstract-type="main" id="cpe2874-abs-0001"> <title>SUMMARY</title> <p id="cpe2874-para-0001">With distinct advantages in resolving the problems of small sample, nonlinear, high dimension learning, the support vector machine (SVM) has been widely applied in face detection and face recognition. In fact, a large number of facial images were needed to train the SVM algorithms. With the rising of training image numbers, the training complexity of SVM was increased by way of geometric series. In this paper, the hybrid Monte Carlo method of the Bayesian support vector machine is proposed. This method solves the problems of high‐dimension and long training time effectively. Experimental results show that the method greatly reduces the training time of face detection algorithm and obtains more accurate face detection effect. Copyright © 2012 John Wiley &amp; Sons, Ltd.</p> </abstract>