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Author ： Patrick Lucey , Jeffrey F. Cohn, Takeo Kanade , Jason Saragih , Zara Ambadar

The Extended Cohn- Kanade Dataset(CK+):A complete dataset for action unit and emotion-specified expression. Author ： Patrick Lucey , Jeffrey F. Cohn, Takeo Kanade , Jason Saragih , Zara Ambadar Conference on Computer Vision and Pattern Recognition 2010 Speaker ： Liu, Yi- Hsien. Outline.

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Author ： Patrick Lucey , Jeffrey F. Cohn, Takeo Kanade , Jason Saragih , Zara Ambadar

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  1. The Extended Cohn-Kanade Dataset(CK+):A complete dataset for action unit and emotion-specified expression Author：Patrick Lucey, Jeffrey F. Cohn, Takeo Kanade, Jason Saragih, Zara Ambadar Conference on Computer Vision and Pattern Recognition 2010 Speaker：Liu, Yi-Hsien

  2. Outline • Introduction • The CK+ Dataset • Emotion Labels • Baseline System • Experiments • Conclusion

  3. Introduction • In 2000, the Cohn-Kanade (CK) database was released • Automatically detecting facial expressions has become an increasingly important research area

  4. Introduction(Cont.) • The CK database contains 486 sequences across 97 subjects. • Each of the sequences contains images from onset (neutral frame) to peak expression (last frame). • The peak frame was reliably FACS(Facial Action Coding System ) coded for facial action units (AUs).

  5. Introduction(Cont.) • Facial Action Coding System (FACS) is a system to taxonomize human facial movements by their appearance on the face

  6. Introduction(Cont.) • While AU codes are well validated, emotion labels are not • The lack of a common performance metric against which to evaluate new algorithms • Standard protocols for common databases have not emerged

  7. The CK+ Dataset • Participants were 18 to 50 years of age, 69% female, 81% Euro-American, 13% Afro-American, and 6% other groups • Image sequences for frontal views and 30-degree views were digitized into either 640x490 or 640x480 pixel arrays with 8- bit gray-scale or 24-bit color values.

  8. The CK+ Dataset(Cont.) • For the CK+ distribution, they have augmented the dataset further to include 593 sequences from 123 subjects (an additional 107 (22%) sequences and 26 (27%) subjects). • For the 593 posed sequences, full FACS coding of peak frames is provided.

  9. Emotion Labels • They included all image data from the pool of 593 sequences that had a nominal emotion label based on the subject’s impression of each of the 7 basic emotion categories: Anger, Contempt, Disgust, Fear, Happy, Sadness and Surprise.

  10. Emotion Labels(Cont.) • Compared the FACS codes with the Emotion Prediction Table from the FACS • After the first pass, a more loose comparison was performed • The third step involved perceptual judgment of whether or not the expression resembled the target emotion category.

  11. Emotion Labels(Cont.)

  12. Emotion Labels(Cont.) • As a result of this multistep selection process, 327 of the 593 sequences were found to meet criteria for one of seven discrete emotions.

  13. Baseline System

  14. Baseline System(Cont.) • Active Appearance Models (AAMs) • The shape s of an AAM is described by a 2D triangulated mesh. • In particular, the coordinates of the mesh vertices define the shape s = [x1; y1; x2; y2; …. ; xn; yn]

  15. Baseline System(Cont.) • SPTS：The similarity normalized shape, refers to the 68 vertex points for both the x- and y- coordinates, resulting in a raw 136 dimensional feature vector • CAPP： The canonical normalized appearance, refers to where all the shape variation has been normalized with respect to the base shape

  16. Baseline System(Cont.) • SVMs(Support Vector Machines) attempt to find the hyper plane that maximizes the margin between positive and negative observations for a specified class.

  17. Experiments • Emotion detection. • To maximize the amount of training and testing data, they believe the use of a leave-one-subject-out cross-validation configuration should be used.

  18. Experiments(Cont.) • SPTS

  19. Experiments(Cont.) • CAPP

  20. Experiments(Cont.) • SPTS+CAPP

  21. Conclusion • In this paper, they try to address those three issues by presenting the Extended Cohn-Kanade (CK+) database • Added another 107 sequences as well as another 26 subjects. • The peak expression for each sequence is fully FACS coded and emotion labels have been revised and validated

  22. Conclusion(Cont.) • Propose the use of a leave-one-out subject cross-validation strategy for evaluating performance • Present baseline results on this using our Active Appearance Model (AAM)/support vector machine (SVM) system.

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