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Project 2 GRIM GRINS

Project 2 GRIM GRINS. Team 2. Michal Hradis Ágoston Róth Sándor Szabó Ilona Jedyk. OUR TEAM. Our team. Michal Hradis Brno University of Technology, Czech Republic. Main Function BOSS. Our team. Ágoston Róth Babes-Bolyai University Kolozsvár, Romania. Main Function

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Project 2 GRIM GRINS

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  1. Project 2 GRIM GRINS Team 2 Michal Hradis Ágoston Róth Sándor Szabó Ilona Jedyk SSIP 2006 09.07.2006

  2. OUR TEAM SSIP 2006 09.07.2006

  3. Our team Michal HradisBrno University of Technology, Czech Republic Main Function BOSS SSIP 2006 09.07.2006

  4. Our team Ágoston RóthBabes-Bolyai University Kolozsvár, Romania Main Function Listening to the Boss SSIP 2006 09.07.2006

  5. Our team Sándor SzabóUniversity of Szeged, Hungary Main Function Listening to the Boss SSIP 2006 09.07.2006

  6. Our team Ilona JedykTechnical University of Lodz, Poland Main Function Listening to the Boss SSIP 2006 09.07.2006

  7. Our task • Localize face • Recognizing of face expressions • neutral • surprised • angry • smiling • Assumptions – pictures of single frontal face SSIP 2006 09.07.2006

  8. 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) SSIP 2006 09.07.2006

  9. Face detection • AdaBoost classifier with Haar-like features • Training - CBL Face Database • Multiple detections SSIP 2006 09.07.2006

  10. 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) SSIP 2006 09.07.2006

  11. AdaBoost conclusion • Adventages • Low computation cost • High number of features (1000 – 1000000) • High number of samples • Disadvatages • Gready selection – suboptimal result SSIP 2006 09.07.2006

  12. 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 SSIP 2006 09.07.2006

  13. Neutral Happy Angry Surprised Decision SSIP 2006 09.07.2006

  14. Program • Program in C++ • Using Open CVLibrary • 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 SSIP 2006 09.07.2006

  15. Results SSIP 2006 09.07.2006

  16. 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 SSIP 2006 09.07.2006

  17. References • Intel, “Open Computer Vision Library, Reference Manual” http://developer.intel.com • Recognizing facial expression: machine learning and application to spontaneous behaviorhttp://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=1467492 • A Short Introduction to Boostinghttp://www.site.uottawa.ca/~stan/csi5387/boost-tut-ppr.pdf SSIP 2006 09.07.2006

  18. Thanks for your attention SSIP 2006 09.07.2006

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