Review of Face Detection Techniques

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 04 | Apr 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 3782
Review of Face Detection Techniques
Tarun Rajpurohit1, Aarya Raj2, Balraj Singh3
1, 2, 3 School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India
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Abstract -Face Detection, which is an effortless task for
humans, is complex to perform in machines. In recent times,
the speed of which we are having the resources of
computational is in the way of the advancement of face
detection technology. There are many fully developed
algorithms made to detect the faces. There is a immense
increase in the video and image database by which there is
an incredible need of automatic understanding and
examination of information by the smart systems. Face plays
a major role in social intercourse for conveying identity and
feelings of a person. The techniques of face detection system
play a major role in face recognition, facial expression
recognition, human-computer interaction, head-pose
estimation etc. Face detection is a computer technology
which determines the size of a human face and the location
of a human Face in a digital image. There are many topics in
the computer vision literature but Face detection has been
standout amongst all the topics.[1] This paper is a review of
various techniques explored for the Face detection in digital
images. Different challenges and the applications of the face
detection are also mentioned and presented in this paper. At
the end, there are many different standard databases for
face detection which are also mentioned with their
respective features and conclude this paper with several
promising directions for future research.
Key Words: Face detection , Feature Analysis, Eigen
Faces, SVM, PCA.
1.INTRODUCTION
Face detection is an issue of computer vision which
involves finding the faces in images. It is also the main and
the starting step for many face-related technologies. For
instance, there are many technologies like face verification
, face modeling, gender and age recognition, head pose
tracking, facial expression recognition and many more. [1]
Face detection is a trifling task for humans, which can
perform naturally with almost no hard work or effort.
However, the task is not simple and its very complex and
complicated to perform via machines and requires many
computationally complex steps to be undertaken. In recent
times, the development in computational technology has
ameliorated the research in the area of the Face detection.
As of now , there are many algorithms and methods for
detecting faces have been proposed . Even so, there is a
little attention given in making of a robust and the updated
survey of these face detection methods.[1] There are many
research projects and commercial products have
demonstrated the capability of a computer which can
interact with the humans in a very simple and natural way
by looking cameras, listening to people through the
microphones, understanding these inputs and then
reacting to the users or the people in a very friendly
manner. There are many techniques but one of the
fundamental techniques which enables such natural
Human-Computer Interaction (HCI) is Face Detection. Face
detection is the first step and initial step also called as the
stepping stone to all facial analysis algorithms, including
the face alignment, face relighting, face modelling, face
recognition, face verification/authentication, head pose
tracking, facial expression tracking/recognition,
gender/age recognition and many more.[2]
Fig -1: Face Detection
2. THE CHALLENGES IN THE FACE DETECTION
TECHNIQUES
There are many challenges in face detection, the
reasons behind it is the requirement for accuracy, and the
detection rate of the face detection. The challenges are not
simple. Some of them are too many faces in images, odd
expression, less resolution, face occlusion, illumination,
skin color , distance and orientation etc.[2]
 Face occlusion :- Face occlusion is hiding face by any
object by which face recognition is not done. It may be
any thing like scarf, hairs, hand, glasses etc. It also
reduces the face detection rate.
 Illumination :- When there is a lightining effect in the
face by which the face cannot be detected properly.

 Odd expressions :- Human face with some odd
expressions unlike natural or simple, which have
become a challenge for face detection.
 Too many faces in the image :- This means that the
image or face which contains too many human faces,
which is challenge for the face detection.
 Complex Background :- this mean that a image having
many objects in their background that will reduces the
accuracy and the rate of face detection.
 Less resolution :- The Resolution of image may be
very low or poor means the quality is not good, which
is also challenging for face detection.
 Skin color :- The Skin-color differs as the geographical
location differs. Skin color of an Indian is different
from the an African and the skin color of an African is
different from the an American and so on. So , the
changing in the skin-color is also challenging for face
detection.
 Orientation :- when the face or an image pose is not
orientated properly then It reduces the accuracy and
detection rate of face detection.
 Distance :- If their is too much distance between the
camera and the face which leads to reduce accuracy
and the detection in rate of the face detection.
Fig -2: Challenges of Face Detection Techniques
3. NEED OF FACE DETECTION TECHNIQUES
 Human Computer Interaction System :- It is a
property , design and the use of the computer
technology , which focusing particularly on the
interfaces between users and computers.
 Biometric Attendance :- This is the system of taking
the attendance of people by their Face or Finger prints
etc.
 Gender classification :- The information of the gender
can be found from the human being image.
 Document control and access control :- Control can be
imposed to document access with face identification
system.
 Face Recognition :- A facial recognition system is a
procedure by which we can identify or verify a person
from a digital image or a video frame.[3] There are
many ways to do it one of the ways to do this is by
comparing selected facial features from the image and
a facial database. It is mainly used for security
systems.
 Photography :- There are now some digital cameras
which use face detection for autofocus. Face detection
is also useful for selecting regions of internet in photo
slideshows.[4]
4. FACE DETECTION TECHNIQUES
Face detection is a computer technology which determines the
location and the size of a human face in the digital image. In
this technology , the facial features are detected and any other
objects like trees, buildings and other bodies are not considered
from the digital image.[5] It can be also known as a specific
case of object-class detection, in which the task is to find the
loaction and sizes of all objects in an image that belongs to a
given class. Therefore, there are two types of approaches to
detect the facial part in the given digital image [6]
A. Feature Based Approach
 Skin color base :- Is a feature which is use for
tracking a face having several advantages. Color is an
important feature of human faces. The processing of
color is much faster than other facial features. Under
certain lighting conditions, color is orientation
invariant.[7] Tracking the human face while using a
color as a main feature has several problems like the
color representation of a face obtained by a camera is
influenced by many factors like, the ambient light,
object movement , etc.
 RGB Color Model :- RGB colors are the terms
knownand given for the three primary colors which are
Red (R), Green (G), and Blue (B).[1] In RGB color space,
there is a normalized color histogram is used to detect
the pixels of skin color of an image and can be further
normalized for changes in intensity on dividing by
luminance. [8]This model localizes and detect the face.
So, it is a basically the basic color model and all other
model are derived from it. The RGB color model is also
light sensitive. There are many other models such as Y
CbCr or HSI models by which RGB model have major
drawback that it cannot separate clearly the mere color
(Chroma) and the intensity of a pixel, that’s the reason

sometimes it is difficult to distinguish skin colored
regions. Due to this factors it is less favorable and it is
widely used for color space for processing and storing
digital images and not used widely in skin detection
algorithms.
Fig -3: RGB Color Model
 HSV color model :- In this model the color are
specified in terms of Hue (H), Saturation (S) and
Intensity value (V) which are the three attributes. In
this, Hue refers to the color of red, green, blue and
yellow having the range of 0 -360. Saturation means
the purity of the color and takes the value of 0 - 100%
whereas Value refers to the brightness of color and
provides the achromatic idea of the color.[9] The Skin
color pixel should satisfy the following conditions:
0 < H < 0.25
0.15 < S < 0.9
 YCbCr color model :- In this model the terms are
specified in terms of Luminance (Y channel) and
Chrominance (Cb and Cr channels). It segments the
image into a luminous component and
chrominance.[10] In YCbCr color model , the
distribution of the skin areas is consistent across
different races in the Cb and Cr color spaces. Range of
Y lies in between 16 and 235 and range of Cb and Cr
lies in between 16 and 240.
 Feature Analysis :- These algorithms aim to find
structural features that exit even when the pose,
viewpoint, or lighting conditions varies, and then use
these to locate faces. These methods are mainly
designed for face localization.
 Feature Searching :- This is an approach proposed by
the Viola and Jones for object detection minimizes
computation time while the approach is going to
achieve its high detection accuracy .[11] This method
proposed by the Viola and Jones is fast and robust for
the face detection which is almost 15 times quicker
than existing techniques at the time of release with
holding the 95% accuracy. To support this approach
Viola and Jones have stated their three key supports.
There are three key supports are as follows :-
1. The first one is that the there is introduction of a new
image illustration called the integral image which
allows the different features used by our detector to
be composed very quickly.[11]
2. The second ia an easy and efficient classifier which is
uded in a algorithm to select a small number of critical
visual features from a very large set of potential
features.
3. The third contribution is a process for combining
classifiers in the terms of a cascade which allows
background regions of the image to be quickly
discarded.
Fig -4: Working of Viola-Jones methodology
Advantages of Feature Searching :-
 Feature Searching is the most admired algorithm for
face detection in real time.
 The main advantage of Viola and Jones approach is its
uncompetitive detection speed while relatively high
detection accuracy, comparable to much slower
algorithms.
 Viola and Jones technique for Face detection is
successful method as it has a very low false positive
rate.
Limitations of Feature Searching :-
 Limited head poses.
 Do not detect black faces.
 Extremely long training time.

B. Image Based Approach
 Neural Network :- This is a network which rationally
attached neural network that examines small
windows of an image, and chooses whether each
window contains a Face.[12]The system arbitrates
between several networks to enhance performance
over a single network. By which doing this it
elimnates the most complex and hard task of manually
selecting non-face training examples, which must be
selected to cover the entire space of non-face images.
In early days this network was the most hierarchical
neural network was proposed. An earliest neural
network for face detection is developed, which consist
of four layers with 1024 inputs units, 256 units in the
first hidden layer, eight units in the second hidden
layer, and two output units. There are several neural
networks present out of which some of the most
widely used and known are :[13]
Probabilistic Decision-Based Neural Network ( PDBNN )
 Ariticial Neural Network ( ANN )
 Regions with convolutional neural network ( R-CNN )
 Eigen Faces Method :- Eigenvectors has been used in
face recognition, in which a simple neural network is
demonstrated to perform face recognition for aligned
and normalized face images.[14] Images of faces can
be linearly encoded in this method with the help of a
modest number of basis images. The set of the optimal
basis vectors is called as Eigen pictures since these are
simply the eigenvectors of the covariance matrix
which is computed from the vectorized face images in
the training set.[15]
 Support Vector Machine (SVM) :- SVMs are also been
used for face detection. SVMs works like a new
paradigm to train polynomial function, neural
networks, or also the radical basis function (RBF)
classifiers.[16] SVMs work on the Induction principle,
called structural risk minimization, which have an
target to minimize an upper bond on the error. It runs
approximately 30 times faster than the system. SVMs
are also been used to detect faces.
 Principal Component Analysis (PCA) :- PCA is a
technique which is based on the concept of Eigen
Faces. PCA on a training set of Face images is
performed to generate the Eigen Faces in face
space.[17] To detect the presence of a face in scene,
the distance between an image region and the face
space is computed for all locations in the image. The
final result of calculating the distance from face space
is a called as the face map.
5. CONCLUSION
In the recent time, face detection has achieved
considerable attention from every parts in the society like
from researchers in bio-metrics, pattern recognition, and
the computer vision groups. In this area, there are
countless security, and forensic applications requiring the
use of face recognition technologies. Now-a-days, as you
can see that the face detection system is very important in
our day to day life. There are many technologies and
among the entire sorts of biometric, face detection and
recognition system is the most accurate in terms of the
accuarcy. In this paper, we have presented a review of face
detection techniques. It is very much interesting and
exciting to see face detection techniques be increasingly
used in real-world applications and products. Application
of the face detection and the challenges of face detection
which are faced are also been discussed which motivated
us to do the research in face detection. In future, it is most
straightforward direction is to further improvement in
face detection in presence of some problems like face
occlusion and non-uniform illumination. It has a very
bright and great future ahead in upcoming times.
Currently, many companies providing facial biometric in
smart phones or mobile phones for purpose of access. In
future it will be used for payments, security , healthcare ,
advertising , criminal identifications etc
6. REFERENCES
[1] Kumar, Ashu, Amandeep Kaur, and Munish Kumar.
"Face detection techniques: a review." Artificial
Intelligence Review 52.2 (2019): 927-948.
[2] Hasan, Md Khaled, et al. "Human face detection
techniques: A comprehensive review and future
research directions." Electronics 10.19 (2021): 2354.
[3] Ismail, Nurulhuda, and Mas Idayu Md Sabri. "Review
of existing algorithms for face detection and
recognition." 8th WSEAS International Conference on
Computational Intelligence, Man-Machine Systems
and Cybernetics. 2009. footnotes in the abstract or
reference list. Use letters for table footnotes.
[4] R. Sharma, V. K. Sharma and A. Singh, "A Review Paper
on Facial Recognition Techniques," 2021 Fifth
International Conference on I-SMAC (IoT in Social,
Mobile, Analytics and Cloud) (I-SMAC), 2021, pp. 617-
621, doi: 10.1109/I-SMAC52330.2021.9640896.
[5] Zhang, Cha, and Zhengyou Zhang. "A survey of recent
advances in face detection." (2010).
[6] Zhenguo Yuan, "Face Detection and Recognition Based
on Visual Attention Mechanism Guidance Model in
Unrestricted Posture", Scientific Programming, vol.
2020, Article ID 8861987, 10 pages, 2020.

[7] Crowley JL, Coutaz J (1997) Vision forman machine
interaction. Robot Auton Syst 19(3):347-358
[8] Subban R, Mishra R (2012) Rule-based face detection
in color images using normalized rgh color space-a
comparative study. In:Proceeding of international
conference on computational intelligence and
computing research, pp 1-5
[9] Hashem HF (2009) Adaptive technique for human face
detection using HSV color space and neural
networks.In: Proceedings of 26th national radio
science conference, pp 1-7
[10] Zhu X, Ren D, Jing Z, Yan L, Lei S (2012a) Comparative
research of the common face detection methods.
In:Proceeding of 2nd international conference on
computer science and network technology, pp 1528-
1533
[11] Zhang H, Xie Y, Xu C (2011) A classifier training
method for face detection based on AdaBoost. In:
Proceeding of international conference on
transportation, mechanical, and electrical engineering,
pp 731-734
[12] Agui T, Kokubo Y, Nagashashi H, Nagao T (1992)
Extraction of face recognition from monochromatic
pho tographs using neural networks. In: Proceeding of
2nd international conference on automation, robotics,
and computer vision, vol 1, pp 1881-1885
[13] Propp M, Samal A (1992) Artificial neural network
architectures for human face detection. In: Proceeding
of artificial neural networks in engineering, vol 2, pp
535-540
[14] Kirby M, Sirovich L (1990) Application of the
Karhunen-Loeve procedure for the characterization of
human faces. IEEE Trans Pattern Anal Mach Intell
12(1):103-108
[15] Hotelling H (1933) Analysis of a complex of statistical
variables into principal components. J Educ Psychol
24(6):417-441
[16] Mingxing J, Junqiang D, Tao C, Ning Y, Yi J, Zhen Z
(2013) An improved detection algorithm of face with
combining AdaBoost and SVM. In: Proceeding of 25th
Chinese control and decision conference, pp2459-
24633
[17] Turk M, Pentland A (1991) Eigen faces for
recognition. J Cogn Neurosci 3(1):71-86

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