1. technology and computing

Volume 5, Issue 3, March 2015
ISSN: 2277 128X
International Journal of Advanced Research in
Computer Science and Software Engineering
Research Paper
Available online at: www.ijarcsse.com
Special Issue: E-Technologies in Anthropology
Conference Held at Bon Secours College for Women, India
Image Segmentation for Multiple Face Detection Using CMY Color
Model Based on Boundary Values
1
S. Saleth Shanthi, 2Dr. M. P. Indra Gandhi
Research Scholar, Assistant Professor (SG),
1, 2
Department of Computer Science, Mother Teresa Women’s University,
Koodaikanal, Tamil Nadu, India
Abstract--- Digital Image Processing (DIP) is a multidisciplinary science that borrows principles from diverse fields
such as optics, surface physics, visual psychophysics, computer science and mathematics. Some of image processing
applications can be finding in: astronomy, ultrasonic imaging, remote sensing, video communications and
microscopy. Face detection/recognition has attracted much attention and its research has rapidly increased in many
potential applications in computer, communication and automatic access control system. Furthermore, face detection
as a first step is an important part of face recognition. Since the image has lots of variations in appearance, face
detection is not straightforward, such as pose variation, occlusion, image orientation, illuminating condition and
others. The full face detection and gender recognition system is made up of a series of connected components. In this
paper the sobel edge detection technique is applied to the image using sobel mask filtering and to find the edge of the
faces. In this paper we propose a method to detect human faces in color images using CMY color model for multiple
faces with multiple boundary.
Keywords: RGB Image, CMY Color Model, Face Detection, Image Segmentation, Image Enhancement, Sobel Edge,
Facial Features, MATLAB.
I.
INTRODUCTION
Images are vital and integral part of everyday life. One person to person basic, image processing is a process which takes
an image input and generates a modified image output. Image processing may be analog or digital. Analog image
processing uses analogue electrical circuits to carry out an operation. This article is about general techniques that apply to
all of them. The acquisition of images (producing the input image in the first place) is referred to as imaging. Image
Processing is a method to convert an image into digital form and perform some operations on it, in order to get an
enhanced image or to extract some useful information from it.
Face detection is a difficult problem that consists of finding one or more faces in an image or in a video sequence under
different situations. This difficulty originates from the fact that the face is not a rigid body and the different conditions
under which the image or the sequence of images was acquired. The main challenges facing any face detection system
typically include pose variation, the presence or absence of structural components, facial expression, occlusion, image
orientation, imaging conditions, and background variations.
Figure1.: Block Diagram of Face detection
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Shanthi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5 (3),
March- 2015, pp. 144-151
The original RGB image is got from the dataset or digital camera. To detect the face using proposed methodology. The
block diagram of proposed method is in figure 1.
The idea of face detection can be combined with almost every smart system as face being a biometric identifier they
ensure a better secure and immune to intrusions. Biometric is the use of distinctive physical features (e.g., iris,
fingerprints, face, and retina) and behavioural feature (e.g., gait, signature), called biometric identifiers, for automatic
recognition of an individual.
Color segmentation may be more accurate because of more information at the pixel level comparing to greyscale images.
The standard Red-Green-Blue (RGB) colour representation has strongly interrelated colour components, and a number of
other colour systems (e.g. HSI Hue-Saturation-Intensity) have been designed in order to exclude redundancy, determine
actual object / background colours irrespectively of illumination, and obtain more stable segmentation.
II. RELATED WORK
The face detection system is presented to detect a face image from any background. This system is important in many
applications such as face recognition to increase the speed and accuracy of recognition process that is because it deletes
any undesired information. The proposed face detection system is depending on classifying the face as being either skin
or non-skin [10]. An image may be defined as a two- dimensional function, f(x, y), where x and y are spatial coordinates,
and the amplitude of f at any pair of coordinates(x, y) is called the intensity or gray level of the image at the point. When
x, y and the amplitude values of f are all finite, discrete quantities, we call the image a digital image [1]. Some of the
color models used in RGB for monitoring color, CMY model for color printing. The CMY color space is subtractive. The
CMY color space can be device independent, but in major frequent they are used in reference to a special device [2].
Face Template Constructing Method: The quality of template immediate influences the effect of matching detection. To
reduce the chanciness of local density of the template, the template based on the information of average face is
constructed, such as average eye template and average face template [4].Face detection using color information usually
consists of two main steps: localization of candidate face regions and validation of the face hypothesis using some
additional information about face structure [3].
Sobel operator- it is a discrete differentiation operator, computing an approximation of the gradient of the image
intensity function. At each point in the image, the result of the Sobel operator is either the corresponding gradient vector
or the norm of this vector. The Sobel operator is based on convolving the image with a small, separable, and integer
valued filter in horizontal and vertical direction and is therefore relatively inexpensive in terms of computations [5]. The
gradient magnitude is given by: |G|=√GX2 + GY2 And its approximation is done by: |G|=|GX| + |GY|. The orientation of
angle i.e. direction of gradient is given by: O = arctan(GX/GY)[7]. RGB color space is the most commonly used color
space in digital images[7]. Color is a useful piece of information for skin detection. The skin detection is the most
common and first approach for detecting meaningful skin color , skin color detection may avoid exhaustive search for
faces in an entire image. In this step, we describe that how non skin color is rejected from an Image so that the image
may contains only skin like areas, which will be our skin color segmented image for further processing [11]. It encodes
colors as an additive combination of three primary colors: red(R), green (G) and blue (B). RGB Color space is often
visualized as a 3D cube where R, G and B are the three perpendicular axes. One main advantage of the RGB space is its
simplicity. However, it is not perceptually uniform, which means distances in the RGB space do not linearly correspond
to human perception [15] [16]. In addition, RGB color space does not separate luminance and chrominance, and the R, G,
and B components are highly correlated. The luminance of a given RGB pixel is a linear combination of the R, G, and B
values [6]. Edge detection technique is the way in which human perceives objects and works well for images having
good contrast between regions [8].
Skin classifiers are responsible for labeling the pixel whether it is skin pixel or a non-skin pixel. Piecewise linear decision
boundary approach is used for human skin classification. This approach explicitly defines a decision boundary(s) of the
human skin color class in the subspace of color space based on the set of sample images used for training of skin-colored
and non-skin colored pixels [9].
Morphology is a broad set of operations that process images based on shapes. The operations of morphological are
erosion and dilation used to smooth the object boundary without changing their respective area. The purpose of using
erosion and dilation is to improve the efficiency of face detection[12]. The main work of skin color detection is to build a
noise filter for removing the non-skin color pixels, so that the remaining skin color pixels will fall in the given color
space. A color space is a specification of a coordinate system where each color is represented by a single value [14]. The
result of the enhancement is subjected to skin segmentation, which contains the possible face candidate, followed by the
skin tone percentage index method for region adjustment and noise removal. In contrast, the gray component of the
enhanced image is subjected to edge detection. Results from both ends are combined and each component is analyzed for
verification as a face or non-face [13].
Blobs: are the connected groups of pixels that remain at end of this stage. And hence should have head sizes (measured
by number of pixels) that are relatively similar. The largest blobs should be these heads and blobs considerably smaller
than the larger blobs may be safely assumed to be more ”noise”[17]. The segmented regions containing holes due to the
presence of eyes and mouth are assumed to be probable segmented face regions and eliminate other segmented
regions[18]. Based on the principle of threshold segmentation, we will segment the skin-likelihood images. With the
original images affected by non-face regions, light, rotation angle of face and many other factors, the skinlikelihood
image can’t reflect good differences between background and the objects, which thus affect the results of a follow-up
partition[19].
© 2015, IJARCSSE All Rights Reserved
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Shanthi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5 (3),
March- 2015, pp. 144-151
III. PROPOSED METHODOLOGY
A. Current Segmentation Techniques
The research on image segmentation for many years has been a high degree of attention. Thousands of different
segmentation techniques are present in the literature, but there is not a single method which can be considered good for
different images, all methods are not equally good for a particular type of image. Thus, algorithm development for one
class of image may not always be applied to other class of images. Hence, there are many challenging issues like
development of a unified approach to image segmentation which can be applied to all types of images, even the selection
of an appropriate technique for a specific type of image is a difficult problem. Thus, in spite of several decades of
research, there is no universally accepted method for image segmentation and therefore it remains a challenging problem
in image processing and computer vision. Based on different technologies, image segmentation approaches are currently
divided into following categories, based on two properties of image.
 Detecting Discontinuities
It means to partition an image based on abrupt changes in intensity, this includes image segmentation algorithms like
edge detection.
 Detecting Similarities
It means to partition an image into regions that are similar according to a set of predefined criterion. This includes image
segmentation algorithms like Thresholding, region growing, region splitting and merging.
B. Segmentation based on Edge Detection
Sobel Operator
The computation of the partial derivation in gradient may be approximated in digital images by using the Sobel operators
which are shown in the masks below:
Figure2 : Sobel Mask Operators
This method attempts to resolve image segmentation by detecting the edges or pixels between different regions that have
rapid transition in intensity are extracted and linked to form closed object boundaries.
Face detection is difficult mainly due to a large component of non-rigidity and textural differences among faces. The
long list of these factors include the pose, orientation, facial expression, facial sizes found in the image, luminance
conditions, occlusion, structural components, gender, ethnicity of the subject, the scene and complexity of image’s
background. The scene in which the face is placed ranges from a simple uniform background to highly complex
backgrounds. In the latter case it is obviously more difficult to detect a face. Faces appear totally different under different
lighting faces; faces closer to the camera appear larger than faces that far away from the camera.
The purpose of face detection is to localize and extract the face region from the background. Face detection techniques
can be roughly classified into four categories namely, Skin color model-based approaches, template matching-based
approaches, feature-based approaches, and statistical model-based approaches. Usually, face detection techniques
integrate some or all of the four approaches to achieve high face detection accuracy and a low false detection rate.
Therefore, color segmentation becomes an important role in the area of quality control, image processing, and pattern
recognition and computer vision.
Face detection has been used in many applications such as biometrics, video surveillance, human computer interfaces,
image database management and smart home applications. Face detection is a necessary first step in face recognition
systems with the purpose of localizing and extracting the face region from the background. Detection rate and the
number of false positives are important factors in evaluating face detection systems. Detection rate is the ratio between
the number of faces correctly detected by the system and the actual number of faces in the image.
C.COLOR MODELS
To utilize color as a visual cue in multimedia, image processing, graphics and computer vision applications, an
appropriate method is used for representing the color signal. The different color specification systems o color models
(Color spaces or solids) address the color signals. Color spaces provide a rational method to specify order, manipulate
and effectively display the object colors taken into consideration.
C.1.The RGB Color Model
The red-green-blue model is formed by a color cube.
© 2015, IJARCSSE All Rights Reserved
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Shanthi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5 (3),
March- 2015, pp. 144-151
Figure3: The RGB-cube
Conversion from ( R, G, B) to( X, Y, Z) is given via the chromatic ties (Xr, Y r, Zr), (Xg,, Y rg, Zg) and (Xb, Yb, Zb) of the
CRTs phosphors by matrix multiplication via:
Let Cr:=Xr, + Yr+ Zr . Then Xr =xr . Cr , Yr =yr .Cr and Zr =zr . Cr =(1-xr – Yr ) . Cr.
This can be calculated from
,
,
.
C.2. The CMY Color Model
This stands for cyan-magenta-yellow and is used for hardcopy devices. In contrast to color on the monitor, the color in
printing acts subtractive and not additive. A printed color that looks red absorbs the other two components
and
and
reflects . Thus its (internal) color is G+B=CYAN. Similarly R+B=MAGENTA and R+G=YELLOW. Thus the C-M-Y
coordinates are just the complements of the R-G-B coordinates:
Figure 4: CMY Color Models
© 2015, IJARCSSE All Rights Reserved
Page | 147
Shanthi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5 (3),
March- 2015, pp. 144-151
Figure 5:RGB to CMY
To print a red looking color (i.e. with R-G-B coordinates (1, 0, 0)) we have to use C-M-Y values of (0, 1, 1). Note that M
absorbs G, similarly Y absorbs B and hence M+Y absorbs all but R.
Black ((R, G, B) = (0, 0, 0)) corresponds to (C, M, Y) = (1, 1, 1) which should in principle absorbs R, G and B. But in
practice this will appear as some dark gray. So in order to be able to produce better contrast printers often use black as
color. This is the CMYK-model. Its coordinates are obtained from that of the CMY-model by K: = max (C, M, Y),
C:=C-K , M:=M-K and Y:=Y-K.
IV.
RESULT AND DISSCUSSION
The proposed methodology shows the good results. The accuracy is found to be above 90%. From the existing work the
accuracy, time, number of detected face and size of the image are less than the proposed work. The retrieval of images
containing human faces requires detection of human faces in such images and then recognizing the face. We
implemented a method that segments skin regions out and locate. We used 32 faces to test the performance of this
implementation and we got 90% of accuracy. Based on the size for 20 faces the test performance is above 95%.The
misses usually included regions with a similar skin likelihood values and regions that certainly were skin regions, but
corresponds to other parts of the body such as arm and legs.
S.No
Size
Total Number
Number of
Number of faces
Number of faces
Time
Accuracy
of Faces
faces detected Partially detected
not detected
(%)
1
256*256*3 20
20
1
6.474915 95
2
176*286*3 32
29
3
7.356055 90
a) Original RGB Image
b) CMY Model
c) Filtered Image
d) Edge Detected Image
© 2015, IJARCSSE All Rights Reserved
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Shanthi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5 (3),
March- 2015, pp. 144-151
e) Binary Image
f) Resultant Image with face detected
a) Original RGB Image with286*176
c) Filtered Image
e) Binary Image
© 2015, IJARCSSE All Rights Reserved
b) CMY Model
d) Edge Detected Image
f)Resultant Image with face detected
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Shanthi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5 (3),
March- 2015, pp. 144-151
V.
CONCLUSION AND FUTURE WORK
This paper presents a method for the detection of human face in an image. It uses simple form Skin-color models
depending on the CMY values. In addition, we choose CMY because it is fast and compatible with human color
perception. The overall performance of the proposed method is fast, and become faster more when number of functions
becomes less which makes it useful some real-time applications. Face detection using color information usually consists
of two main steps: localization of candidate face regions and validation of the face hypothesis using some additional
information about face structure. We have proposed a approach to extract efficiently candidate face regions in images
with varying lighting condition and in presence of complex background, with people of different ethnicities and with
several persons contained within the image. While CMY fitted skin color model and hard thresholding usually fail to
extract completely skin regions in complex illumination conditions, our approach manages to detect skin regions on the
entire face. The aim will be to improve the face detection time i.e. the algorithm time. Further the detection of the face
irrespective of the race or if the person is wearing glasses. The accuracy can also be improved.
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