Smile
Detection by Boosting Pixel Differences
ABSTRACT:
Smile
detection in face images captured in unconstrained real world scenarios is an
interesting problem with many potential applications. This paper presents an efficient
approach to smile detection, in which the intensity differences between pixels
in the grayscale face images are used as features. We adopt AdaBoost to choose
and combine weak classifiers based on intensity differences to form a strong
classifier. Experiments show that our approach has similar accuracy to the
state-of-the-art method but is significantly faster. Our approach provides 85%
accuracy by examining 20 pairs of pixels and 88% accuracy with 100 pairs of
pixels. We match the accuracy of the Gabor-feature-based support vector machine
using as few as 350 pairs of pixels.
EXISTING SYSTEM:
The machine analysis of facial
expressions in general has been an active research topic in the last two
decades.
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
DISADVANTAGES OF EXISTING SYSTEM:
·
This is a challenging problem
·
It seems very difficult to capture the
complex decision boundary among spontaneous expressions
·
Very limited data were used in their
study
PROPOSED SYSTEM:
In this paper, we focus on smile
detection in face images captured in real-world scenarios.
We present an efficient approach to smile
detection, in which the intensity differences between pixels in the grayscale
face images are used as simple features.
AdaBoost then is adopted to choose and
combine weak classifiers based on pixel differences to form a strong classifier
for smile detection
ADVANTAGES OF PROPOSED SYSTEM:
ü Experimental
results show that our approach achieves similar accuracy to the
state-of-the-art method but is significantly faster.
ü Our
approach provides 85% accuracy by examining 20 pairs of pixels and 88% accuracy
with 100 pairs of pixels.
APPLICATIONS:
Smile
detection has many applications in practice, such as interactive systems (e.g.,
gaming), product rating, distance learning systems, video conferencing, and
patient monitoring. For example, the statistics on the audience smile can be a
hint for “how much the audience enjoys” the multimedia content. Smile
detection has received much interest for commercial applications. For example,
in some digital cameras, the “smile shutter” shoots automatically when a smiling
face is detected
MODULES
ü Extracting
effective features
ü Feature-point
detection
ü Geometric
features exploitation module
ü Boosting Pixel Intensity Differences
ü Smile Detection
MODULE DESCRIPTION:
Extracting
effective features
In this module, first the user has to give input
image. The input image is first checked for the features. In case if the image does
not contain human features, then it does not detect it. If the input image
contains Human features, then it detects the features
Feature-point
detection
In this modules, the feature points are detected
automatically. For face detection, first we convert binary image from RGB
image. For converting binary image, we calculate the average value of RGB for
each pixel and if the average value is below than 110, we replace it by black
pixel and otherwise we replace it by white pixel. By this method, we get a
binary image from RGB image. Then, we try to find the forehead from the binary
image. We start scan from the middle of the image, then want to find a
continuous white pixels after a continuous black pixel. Then we want to find
the maximum width of the white pixel by searching vertical both left and right
site. Then, if the new width is smaller half of the previous maximum width,
then we break the scan because if we reach the eyebrow then this situation will
arise. Then we cut the face from the starting position of the forehead and its
high will be 1.5 multiply of its width. In the figure, X will be equal to the
maximum width of the forehead. Then we will have an image which will contain
only eyes, nose and lip. Then we will cut the RGB image according to the binary
image.
Geometric
features exploitation module
For lip detection, we
determine the lip box. And we consider that lip must be inside the lip box. So,
first we determine the distance between the forehead and eyes. Then we add the
distance with the lower height of the eye to determine the upper height of the
box which will contain the lip. Now, the starting point of the box will be the
¼ position of the left eye box and ending point will be the ¾ position of the
right eye box. And the ending height of the box will be the lower end of the
face image. So, this box will contain only lip and may some part of the nose.
Then we will cut the RGB image according the box.
Boosting Pixel Intensity Differences
After extracting intensity difference
features from face images preprocessed by HE, we run AdaBoost to choose the
discriminative features and combine the selected weak classifiers as a strong
classifier. With the selected top 500 features, the trained AdaBoost achieves
the accuracy of 89.7%.
Smile
Detection
That is, the weight for each pixel is
accumulated, and the grayscale intensity in pictures in Fig. 4 is proportional to
the times of that pixel being used. It is evident that the involved pixels are
distributed mainly in the regions around mouth, with a few from the eye areas.
This is reasonable, considering that the major difference between smile and non-smile
faces is the mouth or the lips. To validate this further, we derive the “mean
faces” of smile and non-smile by averaging all smile faces and non-smile faces
in the data set, which are shown in Fig. 5. We can see in the mean faces that,
visually, the main difference lies in the mouth region and the eyes, where
smile faces have open mouth, whereas non-smile faces have mouth closed.
HARDWARE
REQUIREMENTS
•
SYSTEM : Pentium IV 2.4 GHz
•
HARD
DISK : 40 GB
•
FLOPPY
DRIVE : 1.44 MB
•
MONITOR : 15 VGA colour
•
MOUSE : Logitech.
•
RAM : 256 MB
•
KEYBOARD :
110 keys enhanced.
SOFTWARE
REQUIREMENTS
•
Operating system :- Windows XP
Professional
•
Front End :- Microsoft Visual Studio .Net 2008
•
Coding Language : - C# .NET.
REFERENCE:
Caifeng Shan, Member, IEEE, “Smile Detection by Boosting Pixel
Differences”, IEEE TRANSACTIONS
ON IMAGE PROCESSING, VOL. 21, NO. 1, JANUARY 2012.
