Image Processing Reference
In-Depth Information
1 Introduction
Visual location and tracking of objects of interest, particularly, human faces in video se-
quences, are a critical task and an active field in computer vision applications that involves
interaction with the human face by using surveillance, human-computer interface, biometrics,
etc. As the face is a deformable target and its appearance easily changes because of the face-
camera pose, sudden changes in illumination and complex background, tracking it is very dif-
icult. Common methods of face detection include skin color [ 1 , 2 ] , boosting [ 3 , 4 ] , neural net-
works (NNs) [ 5 ] , support vector machines (SVMs) [ 6 , 7 ], and template matching [ 8 ] . The res-
ults of the skin-color-based method are strongly influenced by sudden changes in lighting and
the method often fails to detect people with different skin colors. In many cases, the results
of the boosting, SVMs, and in particular, the NN methods suffer from the disadvantage of
being strongly linked to the set of images selected for learning. For this type of approach, as
face characteristics are implicitly derived from a window, a large number of face and non-
face training examples are required to train a well-performed detector. To describe the face
by template matching several standard paterns of a face are stored. The face detection is then
computed through the correlations between an input image and the stored standard patterns.
This approach, if one the one hand is simple to implement, on the other hand, has proven to
be inadequate for face detection because of its extreme sensitivity to changes in both pose and
orientation. Face detection may be performed on gray-scale or color images. To detect faces
of different sizes and varying orientations in gray-scale images, the input image has to be for
tated several times and it has to be converted to a pyramid of images [ 3 - 7 ] by subsampling
it by a factor. Therefore, the computational complexity will increase with a complicated and
time-consuming classifier. Detection using color information may be independent of face size
and rotation within the color image. This approach avoids the image-scaling problem and ap-
pears to be a more promising method for a real-time face-tracking application. For this reason,
in this study, we have performed face detection by using color images. The main purpose of
face detection is to localize and extract with certainty a subset of pixels which satisfy some
specific criteria like chromatic or textural homogeneity, the face region, from the background
also to hard variations of scene conditions, such as the presence of a complex background and
uncontrolled illumination. Rough set theory offers an interesting and a new mathematical ap-
proach the manage uncertainty that has been used to various soft computing techniques as:
importance of features, detection of data dependencies, feature space dimensionality reduc-
tion, patterns in sample data, and classification of objects. For this reason, rough sets have been
successfully employed for various image processing tasks including image classification and
segmentation [ 9 - 12 ]. Multiscale representation is a very useful tool for handling image struc-
tures at different scales in a consistent manner. It was introduced in image analysis and com-
puter vision by Marr and others who appreciated that multiscale analysis offers many benefits[13-18].
[ 13 - 18 ] . In this study, we have particularly proposed to make scale space according to the no-
tion of rough fuzzy sets, realizing a system capable of efficiently clustering data coming from
image analysis tasks. The hybrid notion of rough fuzzy sets comes from the combination of
two models of uncertainty like coarseness by handling rough sets [ 19 ] and vagueness by hand-
ling fuzzy sets [ 20 ] . In particular the rough sets defines the contour or uniform regions in the
image that appear like fuzzy sets and their comparison or combination generates more or less
uniform partitions of the image. Rough fuzzy sets, and in particular C -sets first introduced by
Caianiello [ 21 ] , are able to capture these aspects together, extracting different kinds of know-
ledge in data. Based on these considerations, we report a new face-detection algorithm based
on rough fuzzy sets and online learning by NN [ 11 ] , able to detect skin regions in the input
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