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Facial expressionclass barca
 

Facial expressionclass barca

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    Facial expressionclass barca Facial expressionclass barca Presentation Transcript

    • Facial expression classificationIn still imagesAngel Gutiérrez, Montse Pardàs
    • IntroductionFace detectionContour detectionExpression estimationSystem implementation - DEAResultsConclusions
    • INTRODUCTION· Basic expressions (Ekman i Friesen – 1971) Joy Anger Neutral Sad Surprise
    • INTRODUCTIONObjective • To develop an automatic system which can recognize a facial expression in a still image.Applications • Monitor for drivers • Stress detection • Facial coder • Entertainment/ Games • etc.
    • INTRODUCTIONGeneric scheme. Methods. INPUT FACE INFORMATION EXPRESSION FACIAL DETECTION EXTRACTION DECISION EXPRESSION IMAGE· Viola and Jones · Gabor Wavelets filters · Hidden Markov Models · Active appearance models · Neural Networks· Region based · Dense motion fields · Probabilistic models · Feature points tracking
    • FACE FEATURES EXPRESSIONDETECTION DETECTION DECISION
    • FACE DETECTIONViola and Jones: · Fast search · Robust to background · Uses local texture features · Trains classifiers for face/non-face classes · Uses a cascade of classifiers structure (Adaboost)Region-based: · Refines the face detection to obtain a better initizialization
    • FACIAL FEATURE CONTOUR DETECTION Model based method Active Shape Models Active Appearance Models
    • FACIAL FEATURE CONTOUR DETECTIONBased on Active Appearance Model softwareimplemented by Stegmann* * M. B. Stegmann, B. K. Ersbøll, R. Larsen, FAME - A Flexible Appearance Modelling Environment, IEEE Transactions on Medical Imaging, vol. 22(10), pp. 1319-1331, Institute of Electrical and Electronics Engineers (IEEE), 2003 - Facial feature contours are represented by a 58 points model
    • FACIAL EXPRESSION INPUT CLASSIFIER KNOWN DATA SYSTEM OUTPUT Class 1, Class 2, LABELED ... DATABASE Class N. Bayesian framework with probabilistic model: Mixture of multivariate gaussians, trained with EM for each class
    • RESULTSDatabase · 192 labeled images.
    • RESULTS
    • RESULTSTEST 1 FACE FEATURES EXPRESSION DETECTION DETECTION DECISION 95,47% H A N Su Sa H 96% 0% 0% 4% 0% A 0% 98% 0% 0% 2% N 0% 0% 94% 6% 0% 95,47 % Su 4% 0% 3% 93% 0% Sa 0% 0% 3% 0% 97%
    • RESULTSTEST 2 FACE FEATURES EXPRESSION DETECTION DETECTION DECISION 84,66% H A N Su Sa H 96% 0% 2% 2% 0% A 6% 79% 4% 4% 8% N 0% 3% 69% 13% 16% 84,66 % Su 0% 0% 4% 96% 0% Sa 0% 3% 14% 0% 83%
    • CONCLUSIONS· Automatic system for facial expression detection in 3 stages: FACIAL FEATURE EXPRESSION FACE DETECTION CLASSIFICATION CONTOURS DET.·Correct classification rate 85 %, with 5 classes.· Only frontal faces. Problems with facial hair and sometimes with glasses