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An optical flow-based approach to robust face recognition under expression variations.

Chao-Kuei Hsieh1, Shang-Hong Lai, Yung-Chang Chen

  • 1Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan, ROC. d903915@oz.nthu.edu.tw

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 1, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel face recognition system robust to expressions using optical flow and synthesized images. The integrated approach significantly enhances recognition accuracy with limited training data.

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Area of Science:

  • Computer Vision
  • Pattern Recognition
  • Biometrics

Background:

  • Face recognition is a challenging area, especially with expressive faces and limited training samples.
  • Existing methods struggle with variations in facial expressions under single-sample-per-class constraints.
  • Robustness against expression variations is crucial for real-world face recognition applications.

Purpose of the Study:

  • To develop an integrated face recognition system resilient to facial expressions.
  • To address the challenge of recognizing faces with single training sample per class.
  • To improve the accuracy of face recognition in the presence of varying expressions.

Main Methods:

  • Developed a constrained optical flow algorithm for expressional face image analysis.
  • Integrated intraperson optical flow information with synthesized face images.
  • Utilized a probabilistic framework to combine multi-modal information for robust recognition.

Main Results:

  • The proposed system demonstrated improved accuracy in face recognition from expressional images.
  • The combination of optical flow and synthesized images proved effective in handling expression variations.
  • Experimental results validated the robustness of the integrated system.

Conclusions:

  • The integrated face recognition system offers a robust solution for handling facial expressions.
  • The method is particularly effective under the constraint of one single training sample per class.
  • This approach advances the field of computer vision for expressive face recognition.