Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face detection for video summary using illumination-compensation and morphological processing
Pattern Recognition Letters
Face authentication using one-class support vector machines
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
| Hi-index | 0.00 |
Almost all the proposed approaches regard face detection as a typical two-class pattern classification task, i.e., face pattern vs. non-face pattern, and learn face detector from face samples and non-face samples. In practice, face pattern model can be established easily, while it is hard to gain a perfect nonface pattern model, for any pattern beyond face pattern (cat, plane, flower etc.) should belong to non-face pattern. In this paper, we propose a novel face detection approach based on One-Class SVM (OCSVM), in which face detection is just considered to be a one-class pattern problem. Support vectors are used to model face pattern, and non-face patches in given images are rejected based on this model. In order to further improve performance, course-to-fine strategy is used in both the training and detection procedure. Extensive experiments show that the proposed method has an encouraging performance.