1. SSIP 2006 09.07.20061 Project 2 GRIM GRINS Michal Hradis Ágoston Róth Sándor Szabó Ilona Jedyk Team 2

2. SSIP 2006 09.07.20062 OUR TEAM

3. SSIP 2006 09.07.20063 Our team Michal Hradis Brno University of Technology, Czech Republic Brno University of Technology, Czech Republic Main Function BOSS

4. SSIP 2006 09.07.20064 Ágoston Róth Babes-Bolyai University Kolozsvár, Romania Babes-Bolyai University Kolozsvár, Romania Main Function Listening to the Boss Our team

5. SSIP 2006 09.07.20065 Our team Sándor Szabó University of Szeged, Hungary University of Szeged, Hungary Main Function Listening to the Boss

6. SSIP 2006 09.07.20066 Our team Ilona Jedyk Technical University of Lodz, Poland Technical University of Lodz, Poland Main Function Listening to the Boss

7. SSIP 2006 09.07.20067 Our task Localize face Recognizing of face expressions –neutral – surprised – angry – smiling Assumptions – pictures of single frontal face

8. SSIP 2006 09.07.20068 Recognizing facial expression – TECHNIUQUES Method for classification –Support Vector Machine – best results –AdaBoost - good –Linear Discriminant Analysis – junk –Neural networks – ???? Method for feature selection (e.g. using PCA)

9. SSIP 2006 09.07.20069 Face detection AdaBoost classifier with Haar-like features Training - CBL Face Database Multiple detections

10. SSIP 2006 09.07.200610 AdaBoost “Strong” classifier constructed as linear combination of “week” classifiers Greedy selection of week classifiers from large set of features Feature (h(x) = {-1, 1}) –simple guess about sample class –high error (0.1-0.5)

11. SSIP 2006 09.07.200611 AdaBoost conclusion Adventages –Low computation cost –High number of features (1000 – 1000000) –High number of samples Disadvatages –Gready selection – suboptimal result

12. SSIP 2006 09.07.200612 Recognizing facial expression AdaBoost classifier with Haar-like features Database of face expression –MMI face database –photos of SSIP participants –Automatic face extraction with our face localization –100 – 200 samples per class

13. SSIP 2006 09.07.200613 Decision Neutral Angry Surprised Happy

14. SSIP 2006 09.07.200614 Program Program in C++ Using Open CV Library AdaBoost Training –Form VUT Brno Inputs: –Expression classifiers (text file) –Face detector (text file) –Detector configuration (text file) –Image with single frontal face Outputs: –Face image –Expression classification

15. SSIP 2006 09.07.200615 Results

16. SSIP 2006 09.07.200616 Conclusion It really works –75% corect recognition –State of the art around 90 % Not so good performance –Low number of training samples –Haar-like features are not well suited for this task Feature work –Use Gabor wavelets as features

17. SSIP 2006 09.07.200617 References Intel, “Open Computer Vision Library, Reference Manual” http://developer.intel.com Recognizing facial expression: machine learning and application to spontaneous behavior http://ieeexplore.ieee.org/search/wrapper.jsp?arn umber=1467492 http://ieeexplore.ieee.org/search/wrapper.jsp?arn umber=1467492 A Short Introduction to Boosting http://www.site.uottawa.ca/~stan/csi5387/boost- tut-ppr.pdf http://www.site.uottawa.ca/~stan/csi5387/boost- tut-ppr.pdf

18. SSIP 2006 09.07.200618 Thanks for your attention