facial action units (au)
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
1
Selective Transfer Machine for Personalized Facial Action Unit Detection
Wen-Sheng Chu, Fernando De la Torre and Jeffery F. CohnRobotics Institute, Carnegie Mellon University
July 9, 2013
2
AU 6+12
Facial Action Units (AU)
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
Maximizes margin of penalized SVM
Minimize distribution mismatch
9
Goal (1): Maximize penalized SVM margin
marginpenalized 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 estimatorfor testing data!
12
Better fitting!
Groundtruth
Selection by reweighting training data
Goal (2): Minimize Distribution Mismatch
13
14
Optimization: Alternate Convex Search
15
Optimization: Alternative Convex Search
16
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.
17
Experiments
• Features– SIFT descriptors on 49 facial landmarks– Preserve 98% energy using PCA
Datasets #Subjects #Videos #Frm/vid ContentCK+ 123 593 ~20 NeutralPeakGEMEP-FERA 7 87 20~60 ActingRU-FACS 29 29 5000~7500 Interview
18
Experiment (1): Synthetic Data
19
• Two protocols– PS1: train/test are separate data of the same subject
– PS2: training subjects include test subject (same protocol in [2])
• GEMEP-FERA
Experiment (2): Comparison with Person-specific (PS) Classifiers
20
Experiment (2): Selection Ability of STM
21
• 123 subjects, 597 videos, ~20 frames/video
Experiment (3): CK+
22
Experiment (4): GEMEP-FERA• 7 subjects, 87 videos, 20~60 frames/video
23
• 29 subjects, 29 videos, 5000~7000 frames/vid
Experiment (5): RU-FACS
24
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
25
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/