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Facial Action Units (AU)

Selective Transfer Machine for Personalized Facial Action Unit Detection Wen-Sheng Chu , Fernando De la Torre and Jeffery F. Cohn Robotics Institute, Carnegie Mellon University July 9, 2013. Facial Action Units (AU). AU 6+12. Main Idea. Related Work: Features. Related Work: Classifiers.

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Facial Action Units (AU)

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  1. Selective Transfer Machine for Personalized Facial Action Unit DetectionWen-Sheng Chu, Fernando De la Torre and Jeffery F. CohnRobotics Institute, Carnegie Mellon UniversityJuly 9, 2013

  2. Facial Action Units (AU) AU 6+12

  3. Main Idea

  4. Related Work: Features

  5. Related Work: Classifiers

  6. Feature Bias Person specific!

  7. Occurrence Bias

  8. Selective Transfer Machine (STM) Formulation Minimize distribution mismatch Maximizes margin of penalized SVM

  9. Goal (1): Maximize penalized SVM margin margin penalized loss

  10. Goal (2): Minimize Distribution Mismatch • Kernel Mean Matching (KMM)* * “Covariate shift by kernel mean matching”, Dataset shift in machine learning, 2009.

  11. Goal (2): Minimize Distribution Mismatch Groundtruth Bad estimator for testing data!

  12. Goal (2): Minimize Distribution Mismatch Groundtruth Selection by reweighting training data Better fitting!

  13. Optimization: Alternate Convex Search

  14. Optimization: Alternative Convex Search

  15. Compare with Relevant Work [1] "Covariate shift by kernel mean matching," Dataset shift in machine learning, 2009. [2] "Transductive inference for text classification using support vector machines," In ICML 1999. [3] "Domain adaptation problems: A DASVM classification technique and a circular validation strategy," PAMI 2010.

  16. Experiments • Features • SIFT descriptors on 49 facial landmarks • Preserve 98% energy using PCA

  17. Experiment (1): Synthetic Data

  18. Experiment (2): Comparison with Person-specific (PS) Classifiers • Two protocols • PS1: train/test are separate data of the same subject • PS2: training subjects include test subject (same protocol in [2]) • GEMEP-FERA

  19. Experiment (2): Selection Ability of STM

  20. Experiment (3): CK+ • 123 subjects, 597 videos, ~20 frames/video

  21. Experiment (4): GEMEP-FERA • 7 subjects, 87 videos, 20~60 frames/video

  22. Experiment (5): RU-FACS • 29 subjects, 29 videos, 5000~7000 frames/vid

  23. Summary • Person-specific biases exist among face-related problems, esp. facial expression • We propose to alleviate the biases by personalizing classifiers using STM • Next • Joint optimization in terms of • Reduce the memory cost using SMO • Explore more potential biases in face problems, e.g., occurrence bias

  24. Questions? [1] "Covariate shift by kernel mean matching," Dataset shift in machine learning, 2009. [2] "Transductive inference for text classification using support vector machines," In ICML 1999. [3] "Domain adaptation problems: A DASVM classification technique and a circular validation strategy," PAMI 2010. [4] “Integrating structured biological data by kernel maximum mean discrepancy”, Bioinformatics 2006. [5] “Meta-analysis of the first facial expression recognition challenge,” IEEE Trans. on Systems, Man, and Cybernetics, Part B, 2012. http://humansensing.cs.cmu.edu/wschu/

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