1. RECOGNIZING FACIAL EXPRESSIONS THROUGH TRACKING Salih Burak Gokturk

2. OVERVIEW MOTIVATION PREVIOUS WORK PROBLEM DESCRIPTION THEORY OF TRACKING TRACKING VIDEOS THEORY OF SVM

3. MOTIVATION What does expression recognition mean? Guessing the meaning of facial deformations. What are the possible applications ? - Any interactive scenerio. - Video conferencing Why do we want to use 3-D information ? - 2-D system is very dependent on the view yet computationally simple.

4. Components of the recognition system Analysis -Face Tracking Intelligence -Support Vector Machine Classifier Shape Parameters

5. OVERVIEW MOTIVATION PREVIOUS WORK PROBLEM DESCRIPTION THEORY OF TRACKING TRACKING VIDEOS THEORY OF SVM

6. SNAKE MODEL Introduced by Kass and Witkin. Energy Minimization Problem: Used by Waters and Terzopoulos for tracking. Snakes are fit to important regions, and tracked from one view to the other.

7. MODEL BASED 3-D FACE TRACKING DeCarlo and Metaxas, ’96, very accurate tracking. Eisert and Girod, ’98, used in video-conferencing application, with efficient and accurate compression. Gokturk et. al., ’00, brings a data driven approach where the face model is learnt from stereo tracking. Model based approaches. Track all the points together with n-dimensional freedom on the shape. Based on Lukas-Tomasi-Kanade optical flow tracker.

8. OVERVIEW MOTIVATION PREVIOUS WORK PROBLEM DESCRIPTION THEORY OF TRACKING TRACKING VIDEOS THEORY OF SVM

9. PROBLEM DESCRIPTION(Tracking 1) ?

10. PROBLEM DESCRIPTION(Tracking 2) X(t) I(x(t)) I(t+1) TIME t+1 ? X(t+1)

11. PROBLEM DESCRIPTION (Recognition) X(t) [ Rigid, Open Mouth, Smile] ? Training DataClassifier Testing New DataOutput

12. OVERVIEW MOTIVATION PREVIOUS WORK PROBLEM DESCRIPTION THEORY OF TRACKING TRACKING VIDEOS THEORY OF SVM

13. ASSUMPTIONS Cameras are calibrated. The person should move slow unless the camera is fast enough for motion capture. The mesh is initialized to the first image. The user performs the expressions known to the computer

14. p - degrees of freedom - shape is learnt from stereo learning in our case Stereo Tracking Data Monocular Tracking Learn Shape

15. I l (x i (t) ) Time t: I l (x i (t+1)) Time t+1: ? - For robustness, u and v are estimated using a neighbourhood around the point. LUKAS TOMASI KANADE OPTICAL FLOW TRACKER

16. LUKAS TOMASI KANADE OPTICAL FLOW TRACKER EXTENDED TO 3D X(t) I(x(t)) I(t+1) TIME t+1 ? X(t+1)

17. OVERVIEW MOTIVATION PREVIOUS WORK PROBLEM DESCRIPTION THEORY OF TRACKING TRACKING VIDEOS THEORY OF SVM

18. STEREO LEARNING

19. FACE TRACKING - The deformation space of a particular individual is learnt - That particular individual is tracked using a mono camera - Tracked parameters : , R, T.

20. UNIVERSAL FACE TRACKER -The deformation space of a subset of people learnt - The shapes are aligned and PCA is applied on this set

21. OVERVIEW MOTIVATION PREVIOUS WORK PROBLEM DESCRIPTION THEORY OF TRACKING TRACKING VIDEOS THEORY OF SVM

22. Support Vector Machines (SVM) - Best discriminating hyperplane between two class of objects - Distinguish the vectors that carry the relevant information (support vectors) - if nonlinear data, map the data to high dimensional domain, then apply the SVM. Training DataClassifier Testing New DataOutput

23. Expected Contributions - Show that 3-D model based tracking is suitable for further applications. - Support vector machine is a suitable classifier for expression recognition What needs to be done - Choose the appropriate data input for SVM classifier. Using  vector might not help. Create more intelligent vectors in that case. - Choose an appropriate kernel (transformation function) for SVM. - Apply SVM in a one to many fashion. - Combination of intelligent features and SVM should give robust results.