Name
Title | ||
|---|---|---|
Face detection algorithm based on hybrid Monte Carlo method and Bayesian support vector machine. |
Abstract | ||
|---|---|---|
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 (c) 2012 John Wiley & Sons, Ltd. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1002/cpe.2874 | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE |
Keywords | DocType | Volume |
face feature,support vector machine,hybrid Monte Carlo method,feature extraction | Journal | 25 |
Issue | ISSN | Citations |
SP9 | 1532-0626 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liejun Wang | 1 | 9 | 5.54 |
| Taiyi Zhang | 2 | 176 | 17.60 |
| Zhenhong Jia | 3 | 29 | 15.13 |
| Liang Ding | 4 | 0 | 0.34 |