SSIP 2006 09.07.2006 1

Project 2 GRIM GRINS

Michal Hradis Ágoston Róth Sándor Szabó

Ilona Jedyk

Team 2

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OUR TEAM

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Our team

Michal Hradis Brno University of Technology, Czech Republic

Main Function

BOSS

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Ágoston Róth Babes-Bolyai University Kolozsvár, Romania

Main Function

Listening to the Boss

Our team

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Our teamSándor Szabó University of Szeged, Hungary

Main Function

Listening to the Boss

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Our teamIlona Jedyk Technical University of Lodz, Poland

Main FunctionListening to the Boss

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Our task

• Localize face• Recognizing of face expressions

– neutral– surprised– angry– smiling

• Assumptions – pictures of single frontal face

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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)

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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

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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

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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