International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 4- January 2016
Face Detection using Skin Segmentation and
Ycbcr Approach
1
Niharika Bharadwaj, 2 Prabhakar Agarwal
1
2
Student, M. Tech , N.I.E.T Gr. Noida
Assistant Professor, N.I.E.T Gr. Noida
ABSTRACT: Face detection is one of the fastest growing computer technologies. There are multiple algorithms proposed and
more yet to come. This technique works by detecting facial features and ignores anything else in the background, such as
buildings, trees and bodies. Face detection can also be regarded as a more general way of localization of faces because in face
localization, it is required to find the locations and sizes of a known number of faces (usually one). This is a branch of computer
sceince which integrates computer science with its multimedia and image processing applications. Face detection is litte more of
a complex process. In this paper we are simulating a scheme for face detection usinh skin segmentation in which we will segment
the skin areas using YcbCr approach.
Keywords : Face Detection, YcbCr, Skin Segmentation
I.
INTRODUCTION
Since the past few decades, the technoogical
deelopment is fascilating the improvement of realtime vision modules which interact with individuals.
One of such technoogy is object detection. Object
detection is one of the computer technologies, which
is connected to the image processing and computer
vision and it interacts with detecting instances of
objects from the specified class, such as human faces,
building, tree, car and etc. These objects can be read
from the digital images or video frames. We are here
using face as an object and working on the scheme of
face detection. The basic aim of face detection
algorithms is to determine whether there is any face
in an image or not. In other words, face detection is a
task where faces shown on pictures or video are
searched for automatically.
Face detection is one of the most important domain in
object detection schemes, many methods have been
proposed before and all of them aim to detect face(s)
in the given image or real time surveillance systems
with different accuracy and false detection rates.
Furthermore, most of the researchers also mentioned,
which machine learning is their main tool to detect
faces in static and video mode.
For past several years, the problems in facial
detection has been an important part if research to
improve this technology as it seres ariety of
applications in commerce and law enforcement.
Moreover, pattern recognition and heuristic based
methods have been proposed for detecting human
face in images and videos. For any face processing
system, face detection is the first stage which is used
ISSN: 2231-5381
in face recognition systems, automatic focusing on
cameras, automatic face confusion in pictures,
accesscontrols, identification of criminals etc.
Howeer the chalenge in the scheme is its diversity in
faces such as its shape, texture, colour, got a
beard\moustache and/or glasses. Additionally the
photographing can also cause additional differences
such as different lighting conditions, head pose and
facial expressions. In addition, most of the face
detection algorithms can be extended to recognize
other objects such as cars, humans, pedestrians, and
etc.
II.
BACKGROUND
Face detection is one of the demanding issues in the
image processing and it aims to apply for all feasible
appearance variations occurred by changing in
illumination, occlusions, facial feature, etc.
Furthermore, face detection algorithms have to detect
faces which appear with different scale and pose. In
the last decade, in spite of all these difficulties,
superb progress has been made and many systems
have shown remarkable performance. The recent
advances of these algorithms have also made
important contributions in detecting other objects
such as buildings, pedestrians, and cars.
Face detection algorithms can tolerate some factors
which including posture, existence or lack of
structural elements, facial expression, Occlusion,
Image orientation, Illumination and the speed and
time of computation. In the next section some factors
have been verified which can effect on the result of
face detection algorithms such as head pose, facial
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International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 4- January 2016
expression, image
Illumination.
orientation,
Occlusion,
and
Skin Pixel Approach
Segmenting the yellow skin
areas
Images are available in various format, there can be
coloured images in RGB or YcbCr format, binary
images, gray scale images. We wil here work on
RGB and YCbCr.
Processing on detected areas
A. RGB Format
Detection of face objects
In RGB images, the different colors Red, Green and
Blue represents each pixel. Every color of these
requires 8 bits for their storage which means a single
pixel may take atleast 24 bits for its storage.
R
G
B
R
G
Output
B
The value of R, G and B each ranges from 0-255. For
example a value of (0, 0, 0) will represents a black
pixel, value (255, 0, 0) will represents a red pixel
and value (0, 255, 0) will represents a green pixel.
B. YCbCr Format
In contrast to RGB format, the YCbCr format is
available with various kind of interleaving. For
example, a 4:2:2 YCbCr format suggests that a single
pixel is represented by two components, Y and C. Cb
and Cr components are interleaved among the pixels.
So if one pixel is represented by a combination of Y
and Cb, then the next adjacent pixel will be
represented by a combination of Y and Cr. Even if
the Cb and Cr components are interleaved human eye
wil not be able to distinguish.
Y
Cb
Y
Cr
Y
Boundary boxes
Cb
Values for Y, Cb and Cr vary from 0-255. Thus, to
store a single pixel, the amount of storage required is
16 bits, which is 8 bit or 1 byte less than that required
by RGB format.
III.
FACE
DETECTION
PROCESS
A. Algorithm for face detection
Step 1 : Input the test image
Use the comman IMREAD in matlab to input the
image with number of faces to be detected. This
image should be in RGB format which is defined
above already.
Step 2: Conversion of image from RGB to YCBCR
We are converting the image from RGB to YCBCR
with three approaches First by skin detection then by
mutiplying the RGB image’s three components
x(red),y(green),z(blue) and then segmenting the
yellow skin areas. Skin detection is the process
which involves finding skin-colored pixels and
regions inside an image or in a video.The process is
normally used as a preprocessing step to find regions
that potentially have human faces and limbs in
images.
- Skin Detection :
- Segmenting the yellow areas
- Multiplying the RGB image’s
three
component
Step 3 : The input image is initially processed to
improve its quality and prepare it to next stages of the
system. First, the system will convert RGB images to
ycbcr and the
-
Input Image
RGB
TO
YCBCR
CONVERSION
Skin Detection Approach
ISSN: 2231-5381
-
Identify the intensity of the image. If
image intensity is high then reduce
intensity. Else, if intensity is low then
increase intensity otherwise no changes
occur.
Removing small connected pixels.
Dilate the image by applying the
morphological algorithm.
Remove holes.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 4- January 2016
-
Breaking the skin areas into three different
pixels.
Step 4 : Detection of face objects
Step 5 : Detection of boundary by following steps :
-
Calculating avreage of every connected area
and converting the data of cell into array.
Saving the beginning point of connected
frame and it’s height and width.
Show the target in rectangular frame.
Considering the areas which have
0.4<W/H<1.8 and their pixels acreage is
bigger than 1000 as face.
Figure 6
Step 6 : Output
B. Simulation
Simulations of the used facial detection scheme have
been performed using MATLAB Software on trail
basis on the set of 10 images however in this paper
we are showing only few of them. The images are
randomly taken from the internet. These images
belongs to various TV series available.
Figure 7
Figure 1
Figure 8
Figure 5
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International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 4- January 2016
[7]
[8]
[9]
Technology – 2012(ETCSIT2012) Proceedings
published in International Journal of Computer
Applications® (IJCA) 12
S. Tolba, A.H. El-Baz, and A.A. El-Harby, ” Face
Recognition: A Literature Review”, International
Journal of Signal Processing 2:2 2006
I.J. Cox, J. Ghosn, and P.N. Yianios, “Feature-Based
face recognition using mixture-distance,” Computer
Vision and Pattern Recognition,1996.
B.S. Manjunath, R. Chellappa, and C. von der
Malsburg, “A Feature based approach to face
recognition,” Proc. IEEE CS Conf. Computer Vision
and Pattern Recognition, pp. 373-378, 1992.
Figure 9
IV.
CONCLUSION
By simulating we realized the disadvantage of this
process is that as it is working in skin detection it also
detects the presence of hands or other objects and
color combination in background which matches the
tone of skin color and sometimes results in false or
incomplete detection. The face detection schemes is
very useful now a days and we realized its
importance by the application where it is used like
Facial recognition for security and access, facial
features detection for authentication purposes, face
detection in cameras and many more. However an
efficient scheme is always required and with our
schemes we realized that there are some faults which
are needed to be taken care of. This is very growing
research field for the students of computer science
and electronics engineering and we will continue our
work further
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