Human Face Detection in Digital Video Using SVMEnsemble

  • Authors:
  • Hong-Mo Je;Daijin Kim;Sung Yang Bang

  • Affiliations:
  • Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-Dong, Nam-Gu, Pohang, 790-784, Korea. e-mail: invu71@postech.ac.kr;Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-Dong, Nam-Gu, Pohang, 790-784, Korea. e-mail: dkim@postech.ac.kr;Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-Dong, Nam-Gu, Pohang, 790-784, Korea. e-mail: sybang@postech.ac.kr

  • Venue:
  • Neural Processing Letters
  • Year:
  • 2003

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Abstract

This Letter proposes automatic human face detection in digital video using a support vector machine (SVM) ensemble to improve the detection performance. The SVM ensemble consists of several independently trained SVMs using randomly chosen training samples via a bootstrap technique. Next, they are aggregated in order to make a collective decision via a majority voting scheme. Experimental results show that the proposed face detection method using SVM ensemble outperforms conventional methods such as using only single SVM and Multi-Layer Perceptron in terms of classification accuracy, false alarms, and missing rates.