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Adam Yeh

Adam Yeh. UCF Computer Vision REU Week 2. Facial Expressions. Problem: given a face image, determine which of 7 basic facial expressions is displayed Neutral, Anger, Disgust, Fear, Joy, Sadness, Surprise FACS (Facial Action Coding System) Identifies 44 basic facial actions Each expression

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Adam Yeh

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  1. Adam Yeh UCF Computer Vision REU Week 2

  2. Facial Expressions • Problem: given a face image, determine which of 7 basic facial expressions is displayed • Neutral, Anger, Disgust, Fear, Joy, Sadness, Surprise • FACS (Facial Action Coding System) • Identifies 44 basic facial actions • Each expression • Basic object recognition problem

  3. Current Research • Wang et al. [1] • Adaboost with Haar wavelet features • Similar to Viola-Jones approach except train for specific expressions, not just faces • >90% precision, real-time • Other similar research, but slight modifications • Different classifiers • Different learning algorithm

  4. Current Research • Using FACS: Kotsia, Pitas [1] • Asks user to identify certain features in one control frame of face • Charts movement of features and looks up which FACS action(s) are present • Uses machine learning to determine actions • Achieves >95% performance • Problem: not full automated because of user input

  5. Current Research • Tong et al. [3] • Uses expert knowledge • Trains on specific action units, not the entire facial expression • Uses Gabor filters as features, Adaboost for feature selection • Use Bayesian network to combine AUs into facial expressions • 93% recognition

  6. Machine Learning Techniques • Support Vector Machines • Map data into higher dimensional space • Find hyperplane to separate/classify data • K-nearest neighbors • Group test sample with class that has most neighbors within a certain distance • Adaboost

  7. Project Possibility • Expand on Tong et al. • AdaSVM? • Has shown improvements over just Adaboost and SVM in other applications • Different classifiers? • Try different filters • Use knowledge of AUs • Different feature combination techniques

  8. Progress • This week • Background research • Coding parts of Viola-Jones (for specific parts needed) • Next week • Finish coding and test on current images • Try different features • Hopefully filter and modify training data as needed

  9. References • [1] Yubo Wang; Haizhou Ai; Bo Wu; Chang Huang . “Real time facial expression recognition with AdaBoost” Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, Vol.3, Iss., 23-26 Aug. 2004. Pages: 926- 929 Vol.3 • [2] Kotsia, I.; Pitas, I. “Real time facial expression recognition from image sequences using support vector machines.”Image Processing, 2005. ICIP 2005. IEEE International Conference on, Vol.2, Iss., 11-14 Sept. 2005 Pages: II- 966-9 • [3] Yan Tong, Wenhui Liao , and Qiang Ji, “Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships”, accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI).

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