Abstract
Large margin classifiers have demonstrated their advantages in many visual learning tasks, and have attracted much attention in vision and image processing communities. In this paper we apply and compare two large margin classifiers, Support Vector Machines and Sparse Network of Winnows, so detect faces in still gray scale images. Furthermore, we study the theoretical frameworks of these classifiers and analyze the empirical results. Experiments on a test set of 24,045 images exhibit good generalization and robustness, and conform to theoretical analysis.
| Original language | English (US) |
|---|---|
| Pages | 665-668 |
| Number of pages | 4 |
| State | Published - Jan 1 2001 |
| Event | IEEE International Conference on Image Processing (ICIP) - Thessaloniki, Greece Duration: Oct 7 2001 → Oct 10 2001 |
Other
| Other | IEEE International Conference on Image Processing (ICIP) |
|---|---|
| Country | Greece |
| City | Thessaloniki |
| Period | 10/7/01 → 10/10/01 |
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
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Cite this
Yang, M. H., Roth, D., & Ahuja, N. (2001). Face detection using large margin classifiers. 665-668. Paper presented at IEEE International Conference on Image Processing (ICIP), Thessaloniki, Greece.