Video-based face recognition using adaptive hidden markov models

  • Authors:

  • Xiaoming Liu;Tsuhan Chen

  • Affiliations:

  • Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA;Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

While traditional face recognition is typically based on still images, face recognition from video sequences has become popular recently. In this paper, we propose to use adaptive Hidden Markov Models (HMM) to perform videobased face recognition. During the training process, the statistics of training video sequences of each subject, and the temporal dynamics, are learned by an HMM. During the recognition process, the temporal characteristics of the test video sequence are analyzed over time by the HMM corresponding to each subject. The likelihood scores provided by the HMMs are compared, and the highest score provides the identity of the test video sequence. Furthermore, with unsupervised learning, each HMM is adapted with the test video sequence, which results in better modeling over time. Based on extensive experiments with various databases, we show that the proposed algorithm provides better performance than using majority voting of image-based recognition results.