transcript
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- Caifeng Shan, Member, IEEE IEEE TRANSACTIONS ON IMAGE
PROCESSING, VOL. 21, NO. 1, JANUARY 2012
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- INTRODUCTION METHOD EXPERIMENTS
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- INTRODUCTION METHOD EXPERIMENTS
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- 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
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- INTRODUCTION METHOD EXPERIMENTS
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- BOOSTING PIXEL DIFFERENCES S. Baluja and H. A. Rowley, Boosting
set identification performance,Int. J. Comput. Vis., vol. 71, no.
1, pp. 111119, 2007 Baluja introduced to use the relationship
between two pixels intensities as features.
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- 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 For an image of 24*24 pixels, there are or 331200 features
extracted
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- 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:
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- INTRODUCTION METHOD EXPERIMENTS
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- 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
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- Data
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- Illumination Normalization Histogram equalization (HE)
Single-scale retinex (SSR) Discrete cosine transform (DCT) LBP
TanTriggs
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- Illumination Normalization
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- Boosting Pixel Intensity Differences Average of (left) all
smile faces and (right) all nonsmile faces
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- Impact of Pose Variation
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- Thank you