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Smile Detection by Boosting Pixel Differences

Smile Detection by Boosting Pixel Differences. Caifeng Shan , Member, IEEE IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 1, JANUARY 2012. Outline. INTRODUCTION METHOD EXPERIMENTS. Outline. INTRODUCTION METHOD EXPERIMENTS. INTRODUCTION.

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Smile Detection by Boosting Pixel Differences

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  1. Smile Detection by Boosting Pixel Differences Caifeng Shan, Member, IEEE IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 1, JANUARY 2012

  2. Outline INTRODUCTION METHOD EXPERIMENTS

  3. Outline INTRODUCTION METHOD EXPERIMENTS

  4. INTRODUCTION Most of the existing works have been focused on analyzing a set of prototypic emotional facial expressions Using the data collected by asking subjects to pose deliberately these expressions In this paper, we focus on smile detection in face images captured in real-world scenarios

  5. INTRODUCTION

  6. Outline INTRODUCTION METHOD EXPERIMENTS

  7. METHOD BOOSTING PIXEL DIFFERENCES S. Balujaand H. A. Rowley, “Boosting set identification performance,”Int. J. Comput. Vis., vol. 71, no. 1, pp. 111–119, 2007 Balujaintroduced to use the relationship between two pixels’ intensities as features.

  8. METHOD they used five types of pixel comparison operators (and their inverses):

  9. METHOD The binary result of each comparison, which is represented numerically as 1 or 0, is used as the feature. Thus, for an image of pixels, there are 􀀀 􀀀 􀀀􀀀 􀀀 or 3312000 pixel-comparison features

  10. METHOD Instead of utilizing the above comparison operators, we propose to use the intensity difference between two pixels as a simple feature For an image of 24*24 pixels, there are 􀀀􀀀 or 331200 features extracted

  11. METHOD AdaBoost( Adaptive Boosting ) AdaBoost learns a small number of weak classifiers whose performance is just better than random guessing and boosts them iteratively into a strong classifier of higher accuracy the weak classifier consists of feature (i.e., the intensity difference),threshold , and parity indicating the direction of the inequality sign as follows:

  12. METHOD

  13. METHOD

  14. Outline INTRODUCTION METHOD EXPERIMENTS

  15. EXPERIMENTS Data Database : GENKI4K consists of 4000 images (2162 “smile” and 1828 “nonsmile”) In our experiments, the images were converted to grayscale the faces were normalized to reach a canonical face of 48*48 pixels

  16. EXPERIMENTS Data

  17. EXPERIMENTS Illumination Normalization • Histogram equalization (HE) • Single-scale retinex (SSR) • Discrete cosine transform (DCT) • LBP • Tan–Triggs

  18. EXPERIMENTS Illumination Normalization

  19. EXPERIMENTS Boosting Pixel Intensity Differences Average of (left) all smile faces and (right) all nonsmile faces

  20. EXPERIMENTS Impact of Pose Variation

  21. EXPERIMENTS Impact of Pose Variation

  22. Thank you

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