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

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INTRODUCTION
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METHOD
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EXPERIMENTS


INTRODUCTION

METHOD

EXPERIMENTS

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


INTRODUCTION

METHOD

EXPERIMENTS

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

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

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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

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

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:


INTRODUCTION

METHOD

EXPERIMENTS

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

Data

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

Illumination Normalization

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

Impact of Pose Variation

Impact of Pose Variation