Face detection using large margin classifiers

M. H. Yang, D. Roth, N. Ahuja

Research output: Contribution to conferencePaper

4 Citations (Scopus)

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 languageEnglish
Pages665-668
Number of pages4
Publication statusPublished - 2001 Jan 1
EventIEEE International Conference on Image Processing (ICIP) - Thessaloniki, Greece
Duration: 2001 Oct 72001 Oct 10

Other

OtherIEEE International Conference on Image Processing (ICIP)
CountryGreece
CityThessaloniki
Period01/10/701/10/10

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All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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.