International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 14 - Mar 2014
Simple Face Detection Using Skin, Hair
and Edge Characteristic
Mukta K.Dutonde#1, Lokesh Bijole*2
#
*
Student, Computer Engineering, Padm. Dr. V.B. Kolte COE, Malkapur, India
Assistant Professor, Computer Engineering, Padm. Dr. V.B. Kolte COE, Malkapur, India
Abstract-This article presents two different algorithms
for rapid and accurate face detection: The decision
algorithm and edge tracking algorithm. The decision
algorithm based on skin and hair color characteristics
and decision structure which converts the obtained
information from skin and hair regions to labels for
identifying the object dependencies and rejecting many
of the incorrect decisions. The edge tracking algorithm
used to extract the sub windows from the image to
extract the rectangle features while skipping the
backgrounds.
Keywords—Edge detection, label, object dependencies,
threshold, skin region.
I. INTRODUCTION
The human Face is one of the most
important objects of computer vision. It has
a wide range of applications in biometrics
recognition,
computer
vision
and
multimedia applications. Detection of face is
quite a complex procedure and involves the
feature extraction, based on which face can
be detected. There is several approaches for
human face detection, which are categorized
into four types (1) Knowledge-based
method: This method finds invariant
features of a face and localizing the position
of face. The features are used to check
whether human face is appearing in an
ISSN: 2231-5381
image or not [1]. (2)Feature invariant
method: The method involves facial feature
that are invariant to pose, brightness and
viewpoints. Skin colors [2] fall into this
category. (3)Template Matching: This
method calculates Correlation between a test
image and pre-selected facial templates
[3].(4)Appearance based method: This
method uses machine learning techniques to
extract discriminative feature from a
relabeled training set. The Eigen faces
method [4] and AdaBoost-based face
detection [5] is belonging to this class. In
this paper the two different methods and the
advantages of their algorithm is discussed.
The algorithm based on knowledge based
method adopts characteristics of skin and
hair colors [6].while the algorithm based on
appearance
based
method
adopts
characteristics of edges [7].
This paper is organized as section 2 presents
Decision Algorithm based on skin and hair
color, section 3 presents the edge tracking
algorithm,
section 4
presents the
performance analysis, section 5 presents the
conclusion.
II.
DECISION ALGORITHM
The input image is divided into two parts
which are determined by the conditions of
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 14 - Mar 2014
the skin and hair color regions. Depending
on the chosen color area, we can use
Different methods for skin detection. The
intensity element in RGB model allows us to
find a suitable region for dark color hairs.
The outcomes from here will be segmented
by 5×3 or 5×5 blocks. Hereafter, we use
edge detection with the “Canny” filter to
recognize the external and marginal
Edges. This filter plays an important role in
the detection procedure because it preserves
the edge continuity better in comparison
with other filters such as “Sobel” and
“Prewitt”. Then each result matrix will be
labeled and converted to a matrix of objects.
Then algorithm makes decision which
of the labeled objects has correlation with
others. The performance of the algorithm is
explained in the next parts in details.
Fig. 1 exhibits the total view used here for
recognition.
labels.3)[y] Hair+3:It is same as that certain
hair labels matrix, when shift all of it’s ‘Y’
components by 3 pixels. The objects having
scale less than THS or THH are considered
as illegible for face locations and need to
remove its information from the next
consideration. After that it finds the
intersection of skin and the matrix ‘[Y]
Hair+3’.The condition to find out
relationship between two labels are defined
by two parameters that are Mysi and MYHj .
The MYSi and MYHj statements are:
(1) MYSi =
(2) MYHj =
Where [x] =max {nЄz/n≤x} or the floor of
x.
The condition MYSi-10> MYHj allows hair
labels to proceed through the algorithm that
are placed in a higher location. For each skin
label, the eligibility of the whole Hair labels
are examined and the other skin labels
follow the same procedure. After examining
all the skin and hair labels, it uses the Border
and Center detection module to obtain the
Border of any face in isolation. The formula
is:
Fig.1.Diagram of the system
The decision algorithm [6] includes
threshold for skin and hair labels as it extract
the hair and skin characteristic for face
detection.1) THS: The threshold for skin
labels 2) THH: The threshold for hair
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Where Fi means the ith face detected and ‘~’
shows the assumed labels belong to Fi.
Also, in the following equations, the
mathematic symbol ‘|’ means “such that”.
In continue, when we say for an example Fi
X ∈ I or Fi Y ∈ I , we have assumed that
Xor Y contains non-zero information.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 14 - Mar 2014
obtained mask to a new location without any
change in its proportions, such that the
previous center is placed in the location of
the statistical center of the skin label matrix
calculated as follows:
XNew Center =
YNew Center =
Now the detection process is complete.
III.
EDGE TRACKING ALGORITHM
We first concentrate on Preprocessing of
images which include enhancement and
feature extraction.
A. Enhancement
The input face images may have the control
of lighting condition. So the histogram
equalizations is used to compensate for
lighting conditions and improve quality of
image. Also the images contain the noise.
The fine details of the image represent high
frequencies which mix up with those of
noise. The low pass filtering and medium
filtering is used to remove the noise but in
low pass filtering some details of image are
also erased. So the median filtering is used
to suppress the noise. In median filtering the
value of a pixel is replaced by the median of
the values in the neighborhood of the pixel.
B. Feature extraction
Fig.2.Decision Algorithm Flowchart
To mask the detected faces center shifting is
used. Then the effective features in
determining the face location by this
algorithm are the skin labels. With this
assumption, we transfer the previous
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Here we are using the rectangle feature.
There are tree three kinds of rectangle
features the value of a two rectangle feature
is the difference between the sums of the
pixels within two rectangle regions. The
regions have the same size and shape and
are horizontally or vertically adjacent as
shown in the figure. A three rectangle
feature computes the sum within two outside
rectangles subtracted from the sum in a
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 14 - Mar 2014
center rectangle. Finally a four rectangle
feature computes the difference between
diagonal pairs of rectangles.
These
rectangle features are computed using an
intermediate representation for the images
which we call the integral image.
Fig:3 Four types of rectangle features
Edge tracking algorithm[7] eliminate the
background noise and to reduce the number
of sub-windows to be scanned while subsampling.
window. In the same row, then it searches
for the next edge that it is called as top-right
coordinate. Then the algorithm searches in
the column for the edge pixel. if it finds an
edge pixel at any point in the column is
considered as bottom-right & bottom-left
coordinates using the above
four
coordinates, the sub-window is extracted
from the edge image and their mean is
calculated. The sub-window is considered as
background if the mean value is zero and
skipped that window to extract the next subwindow. If mean value is not equal to zero
then the corresponding sub-window is
extracted from the original image with the
same coordinates and the rectangle features
have been calculated.
The following figure
tracking algorithm:
illustrates
edge
The algorithm is given as follows:
Edge Tracking Algorithm
1. Load the edge image Eij
2. For each row
3. For each column
i. Search for bottom-left, bottom-right
top- right and top- left coordinates.
ii. Extract the sub-window from the
edge image.
iii. Calculate its mean (m)
iv. If m=0, then the sub-window is
Considered as background.
Goto Step 3 to extract the next subWindow.
v. Else, extract the corresponding
Sub window from the original image and
Calculate the rectangle features.
The edges are detected from the face image.
In the edge image, the algorithm searches
for first edge pixel in the each row. when it
finds an edge pixel, the edge-pixel is
considered as top-left coordinate of the sub
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Fig:4 Output from edge tracking Algorithm
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 14 - Mar 2014
IV.
PERFORMANCE ANALYSIS
Face detection using skin and hair gives no
guarantee that it detects only skin and hair
pixels. Because the data from other parts of
the image is also included with this color in
it. So the decision algorithm is used to detect
the face characteristics and reject other. In
the decision algorithm, labeling technique is
used to label the face elements i.e. hair and
skin, to several labeled objects. Object scale
comparability is the advantage of this
algorithm. This property helps us to remove
the small size objects by a fraction of the
greatest object size with respect to a
threshold determined arbitrarily. Another
advantage is preserving the combinability
with any other method of face recognition in
any step to improve the reliability. Here we
deplete the objects of the information
matrices quickly with special filters such
that the remained edge contains the same
useful information as the primary matrix had
before. That means the load of processing
decreases and subsequently the speed of
diction can increase. This algorithm has
logic to determine the object dependencies.
While in edge tracking algorithm
almost all the systems are classifying the
faces starting with some base size. Also the
entire image is sub-sampled to identify the
faces. To eliminate the background and to
reduce the number of sub-windows to be
scanned. While sub-sampling, thus it
performance faster. From the above
discussion we can say that the decision
algorithm is better than edge tracking
algorithm because the decision algorithm
extract features of skin and hair from input
image and then identify the edges to remove
the unnecessary information from the image
input. As seen before the edge tracking
algorithm just try to find out the edges of
image which is unable to remove the noise
from the image and it also contain some
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unnecessary
information that
complexity during image detection.
V.
cause
CONCLUSION
In this paper, we determine the two different
algorithms and their advantages. The
decision algorithm is capable of detecting
several human faces at the same time and
sharing information with other methods to
increase the overall system reliability as
could be seen from the algorithm flowchart
it uses simple logical operations on small
amount of data which is the labeled objects
to recognize the faces. The main advantage
of the edge tracking algorithm is detecting
the face with minimal number of scanning.
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