Combining classifiers for face recognition

  • Authors:
  • Xiaoguang Lu;Yunhong Wang;A. K. Jain

  • Affiliations:
  • Dept. of Comput. Sci. & Eng., Michigan State Univ., USA;IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA;IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA

  • Venue:
  • ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
  • Year:
  • 2003

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Abstract

Current two-dimensional face recognition approaches can obtain a good performance only under constrained environments. However, in the real applications, face appearance changes significantly due to different illumination, pose, and expression. Face recognizers based on different representations of the input face images have different sensitivity to these variations. Therefore, a combination of different face classifiers which can integrate the complementary information should lead to improved classification accuracy. We use the sum rule and RBF-based integration strategies to combine three commonly used face classifiers based on PCA, ICA and LDA representations. Experiments conducted on a face database containing 206 subjects (2,060 face images) show that the proposed classifier combination approaches outperform individual classifiers.