An Efficient Approach to Smile Detection

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

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

INTRODUCTION(2/2)

• Example of GENKI4 database images

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

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

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%

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

EXPERIMENTS(3/7)

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%.

EXPERIMENTS(5/7)

• Proposed approach achieves 85% accuracy in smile detection with 20 pixel comparisons and 88% accuracy with 100 pixel comparisons

EXPERIMENTS(6/7)

• the top 20 pairs of pixels being selected in one cross-validation experiment are plot.

EXPERIMENTS(7/7)

• Accurary vs number of classifiers

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.

THANK FOR YOUR KIND ATTENTION!!!