1. Caifeng Shan, Member, IEEE IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 1, JANUARY 2012

2.  INTRODUCTION  METHOD  EXPERIMENTS

3.  INTRODUCTION  METHOD  EXPERIMENTS

4.  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

6.  INTRODUCTION  METHOD  EXPERIMENTS

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

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

9.  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.  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. 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:

14.  INTRODUCTION  METHOD  EXPERIMENTS

15. 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. Data

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

18. Illumination Normalization

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

20. Impact of Pose Variation

22. Thank you