Skip to main content

Feature Extraction Methods for Real-Time Face Detection and Classification

Abstract

We propose a complete scheme for face detection and recognition. We have used a Bayesian classifier for face detection and a nearest neighbor approach for face classification. To improve the performance of the classifier, a feature extraction algorithm based on a modified nonparametric discriminant analysis has also been implemented. The complete scheme has been tested in a real-time environment achieving encouraging results. We also show a new boosting scheme based on adapting the features to the misclassified examples, achieving also interesting results.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to David Masip.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Masip, D., Bressan, M. & Vitrià, J. Feature Extraction Methods for Real-Time Face Detection and Classification. EURASIP J. Adv. Signal Process. 2005, 531492 (2005). https://doi.org/10.1155/ASP.2005.2061

Download citation

  • Received:

  • Revised:

  • Published:

  • DOI: https://doi.org/10.1155/ASP.2005.2061

Keywords and phrases

  • face detection
  • face recognition
  • boosting
  • feature extraction