an efficient approach to smile detection
TRANSCRIPT
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AN EFFICIENT APPROACH TO SMILE DETECTION Caifeng Shan
Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference
Presenter:Muthukumar.SAdvisor :Jar Ferr Yang
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INTRODUCTION(1/2)
• Detecting smiles can be used to estimate the person’s mental state
• The pixel intensities in the gray-scale face image are compared, and the intensity differences are used as features
• Adaboost is adopted to choose and combine intensity differences (based weak classifiers) to form a strong classifier for smile detection
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INTRODUCTION(2/2)
• Example of GENKI4 database images
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OUR APPROACH(1/2)
• We use the intensity differences as features• the next-step is to minimize the number of
features• Adaboost uses weak classifiers, boosts them
into a strong classifier• the distribution is updated to increase the
weights of the misclassified samples and reduce the importance of the others
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OUR APPROACH(2/2)
• The weak classifier is designed to select the feature which best separates the positive and negative examples
• a weak classifier (x)consists of a feature which corresponds to the pixel intensity difference, a threshold θj and a parity indicating the direction of the inequality sign
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EXPERIMENTS(1/7)
Baseline • Gabor features, Local Binary Patterns (LBP) are
used as baseline Illumination Normalization • Histogram equalizationBoostingWith 500 features, the detection accuracy is 89%
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EXPERIMENTS(2/7)
• LBP(8, 2, u2) operator was adopted to extract LBP features.
• Support Vector Machine (SVM) was adopted as classifier,
• Gabor features provide the accuracy of 89.55%• LBP feature achieves 87.10%. But the
dimension of LBP is smaller than Gabor features
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EXPERIMENTS(3/7)
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EXPERIMENTS(4/7)
• Boosting Pixel Intensity Differences
• After extracting intensity difference features Adaboost used to get discriminative features,
• With the selected top 500 features, the trained Adaboost achieves the detection accuracy of 89.70%.
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EXPERIMENTS(5/7)
• Proposed approach achieves 85% accuracy in smile detection with 20 pixel comparisons and 88% accuracy with 100 pixel comparisons
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EXPERIMENTS(6/7)
• the top 20 pairs of pixels being selected in one cross-validation experiment are plot.
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EXPERIMENTS(7/7)
• Accurary vs number of classifiers
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CONCLUSION
• Pixel indensity differences are used as features
• With 20 pixels , it achieves 85% accuracy• pose variation is one of the main difficulties that needs to be addressed in future work.
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THANK FOR YOUR KIND ATTENTION!!!