smile detection by boosting pixel differences
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
Patch-Based Background Initialization in Heavily Cluttered Video
Smile Detection by Boosting Pixel DifferencesCaifeng Shan, Member, IEEE
IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 1, JANUARY 2012OutlineINTRODUCTION
METHOD
EXPERIMENTS
OutlineINTRODUCTION
METHOD
EXPERIMENTS
INTRODUCTIONMost 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 scenariosINTRODUCTION
OutlineINTRODUCTION
METHOD
EXPERIMENTS
METHODBOOSTING 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.METHODthey used five types of pixel comparison operators (and their inverses):
METHODThe 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
METHODInstead 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
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:
METHOD
METHOD
OutlineINTRODUCTION
METHOD
EXPERIMENTS
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
EXPERIMENTS Data
EXPERIMENTSIllumination NormalizationHistogram equalization (HE)
Single-scale retinex (SSR)
Discrete cosine transform (DCT)
LBP
TanTriggs
EXPERIMENTSIllumination Normalization
EXPERIMENTS Boosting Pixel Intensity Differences
Average of (left) all smile faces and (right) all nonsmile faces
EXPERIMENTSImpact of Pose Variation
EXPERIMENTSImpact of Pose Variation
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