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SSIP 2006 09.07.2006 1
Project 2 GRIM GRINS
Michal Hradis Ágoston Róth Sándor Szabó
Ilona Jedyk
Team 2
SSIP 2006 09.07.2006 2
OUR TEAM
SSIP 2006 09.07.2006 3
Our team
Michal Hradis Brno University of Technology, Czech Republic
Main Function
BOSS
SSIP 2006 09.07.2006 4
Ágoston Róth Babes-Bolyai University Kolozsvár, Romania
Main Function
Listening to the Boss
Our team
SSIP 2006 09.07.2006 5
Our teamSándor Szabó University of Szeged, Hungary
Main Function
Listening to the Boss
SSIP 2006 09.07.2006 6
Our teamIlona Jedyk Technical University of Lodz, Poland
Main FunctionListening 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
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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)
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AdaBoost conclusion
• Adventages– Low computation cost– High number of features (1000 –
1000000)– High number of samples
• Disadvatages– Gready selection – suboptimal result
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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
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Decision
Neutral
Angry Surprised
Happy
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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
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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 Boosting http://www.site.uottawa.ca/~stan/csi5387/boost-tut-ppr.pdf
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Thanks for your attention