Review of Face Detection using PCA and ANN Techniques
Sachin Shende1 & Rahila Patel2
Department of Computer Science, Nagpur University, Chandrapur, India
E-mail : shende.sachin83@gmail.com, rahila.patel@gmail.com
Abstract – Face detection is an important part of face
recognition system used in computer vision. There
are many challenges for effective and efficient face
detection. In this paper, the framework for efficient
face detection using fusion of PCA and Artificial
neural network is presented. The image features are
represented as reduced features space by using PCA
which is a dimensionality reduction technique.
Further these features are given as input to the ANN
for training. We have used multilayer perceptron
network for accomplishing this task.
Keywords:- Face detection, LDA, PCA,
recognition , SVM, Adaboost.
I.
methods have been proposed, such as face detection in
color images based on the fuzzy theory
[7], the discriminating feature analysis and Support
Vector Machine (SVM) classifier for face detection [8],
neural network-based face detection [12]. Face color
information is an important feature in the face detection.
In reference [11], a latest survey of skin-color modeling
and detection methods was presented. Statistical color
modules with application to skin detection was reported
in reference [10]. The quantized skin color regions for
face detection were given in reference [13]. Eye is
another important feature for face detection and
recognition. For example, a robust method for eye
feature extraction on the color image was reported in
reference [14]. Using optimal Wavelet packets and
radial basis functions for eye detection was introduced
in reference. Face detection has the application in
variety of fields used in the teleconference system,
medical imaging intelligent video surveillance, smart
cards and security based public places such as airport
and railway station. The other face detection techniques
are Adaboost, fisher technique, float boost etc. Over the
past decade, many approaches to improve the
performance of face detection were proposed. These
approaches are categorized into two types : 1)
Knowledge-based approach, and 2) Feature invariant
approach. Knowledge-based methods use a priori rules
to carry out face detection, such as the face is usually
symmetrical with each other eyes. Feature invariant
approach include extraction of features, filtering of
images with respect to size , noise, distortion
illumination
etc. Face detection
is important
considering the new medical science research such as
plastic surgery. It has become challenging to detect and
recognize face many due to the changes and variation in
the face that occurs due to aging, hormonal changes,
emotion changes ,skin color changes etc. In this paper
the different techniques used in the face detection are
explained. Review of recent face detection method is
studied in this paper.
ANN, face
INTRODUCTION
Human Face detection is the process of identifying
the features of faces to detect the faces on the basis of
the discriminant features. Features of faces are eyes,
ears, eyebrows, nose, lips, hairs, chicks, forehead etc.
Face detection can be carried out using these features of
faces. Face is important part to identify the person. It
can be used as the computer visual application. Face is
the important part of our body by which it is easy to
identify and recognize the person. Face detection is one
of the challenging tasks as there are many issues such as
changes in the appearances of faces, variations in poses,
noise,
distortion
and
illumination
condition.
Complications occur in discriminating the two identical
faces for example in case of twins. There are several
techniques for face detection that exist in the literature.
Principal discriminant analysis (PCA) [1] and Linear
discriminant analysis (LDA) [1] are most commonly
used techniques for face detection. Handsdorff distance
measure for face recognition [2], Elastic Graph
Matching (EGM) [3], eigenspace-based face recognition
[ 4], a novel hybrid neural and dual eigen spaces
methods for face recognition [5], eigenfaces and
Fisherfaces methods [6]. In order to capture the frontal
face image accurately and timely, many face detection
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International Journal on Advanced Computer Theory and Engineering (IJACTE)
II. RELATED WORK
recognition system. This method consist of detection of
faces and recognition of detected faces. In detection step
they have used skin color segmentation with gosian
skin color model combined with Adaboost Algorithm.
Further the series of morphological operators are used to
improve the face detection performance.
Adaboost technique for face detection [15]was
introduced by viola and jones. They proposed a training
classifiers to detect the discriminating features of the
faces. A framework was introduced such that the
training classifiers checked the faces in the cascading
style. They used the boosting method in their work by
which they tried to detect the faces using the
discriminating features. As shown in the fig.3
c0,c1,c2…cn are the classifiers in the cascading manner.
The input is provided to the first classifier. The first
classifier checks whether the input is face or non-face
object .If it is face object then it is passed to the next
classifier otherwise it is discarded as non-face object.
The process of testing faces by several classifiers
continues till it passes to the final node. The output is
the face object only if it passes through every node. In
this method much of the effort has been spent on
improving the boosting method.
But the major
drawback of Adaboost method is
that training
classifiers reject the most of the non-face objects before
reaching the final node. Secondly the accuracy in the
detecting the faces was not much as expected. Adaboost
method takes much time to check the face and non-face
object Thus time complexity is also a major issue in the
Adaboost technique as more number of classifiers are
used. Still many analyst used this method in their work.
P.E Robinson introduced the comparison of PCA
AND LDA techniques of face detection [18] by
implementing the nearest neighbor classifier. He used
the set of database containing the face images in
different rang of poses, lighting condition and occlusion.
Then he compared performance of face detection using
PCA and LDA. In PCA, a training set is used to create a
covariance matrix containing the strongest eigenvalues
and thus form a vector face also called as Face Space.
Secondly the face to be recognized will be is converted
into vector and is compared with the nearest closest face
class present in the dataset .The result obtained will be
the face that is to be detected. It was noted that PCA has
better compact detection rates and less processing time
as compared to PCA AND LDA combined work.
Sanjay A. Pardeshi and Dr. S.N. Talbar [19]
introduced a automatic face detection technique. In this
method first they extracted the fiducial face point of the
color images. Then using Principal component Analysis
(PCA) technique tried to reduce the dimensionality of
Gabor filter .The face is recognized by measuring the
similarity between the Gabor filter (jets) in the Eigen
faces. The proposed work showed that the automatic
face detection recognition system is better for machine
face recognition. The result showed 72% face detection
rate is acquired using the 30 facial feature point with L1 norm and COS distance metrics but all distance
metrics are necessary to be equally good.
According to Khalid Mohamed Alajel, Wei Xiang,
and John Leis[20] face can be detected using the skin
color. They divided the process in three stages .First
image acquisition is done in which the set of color
images containing the face and non-face images
.Secondly they using skin color segmentation non-face
region in the color images are detected using the low
probability region. Finally the modified Hausdorff
distance is used to detect the faces using output of skin
color segmentation. The performance of this algorithm
achieved 87.5 % accuracy in detection faces.
Fig.3. Cascading of boosted classifier
The existing algorithm based on Adaboost
algorithm suffers the problem of computation
complexity for training of weak classifier. However
some researchers have proposed much simpler approach
for face detection without using Adaboost algorithm.
Padma Polash Paul and Marina Gavrilova presented
PCA based geometric modeling for automatic face
detection[16].They have presented PCA base modeling
of geometric structure of the face foe automatic face
detection. They have used skin color modeling for
efficient feature extraction.
Kanhaiya kumar presented artificial neural based
face detection using Gabor feature Extraction [21].They
have discussed the desirable characteristic of spatial
locality and orientation selectivity of the Gabor filter
The feature vector based on Gabor filter is used as the
input of the classifier which is the feed forward neural
network on reduced feature of space.
Alireza Tofighi and S.Amirhassan Monadjemi
proposed face detection recognition using skin color and
Adaboost algorithm [17] combined with Gabor features
and SVM Classifier. They have proposed this technique
to enhance the performance of face detection and
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International Journal on Advanced Computer Theory and Engineering (IJACTE)
Karhunen[22] introduced the method of Principal
Component Analysis(PCA) technique of detecting the
faces. Principal Component Analysis(PCA) is used t
reduce the redundancy and used to reduce minimize the
distance between the data space and projection space.
The merit of Principal Component Analysis(PCA) is
that it can detect the faces even in presence of random
noise and rotation scaling translation distortion.
P.E Robinson introduced the comparison of PCA AND
LDA techniques of face detection [24] by implementing
the nearest neighbor classifier. He used the set of
database containing the face images in different rang of
poses, lighting condition and occlusion. Then he
compared performance of face detection using PCA and
LDA. In PCA, a training set is used to create a
covariance matrix containing the strongest eigenvalues
and thus form a vector face also called as Face Space.
Secondly the face to be recognized will be is converted
into vector and is compared with the nearest closest face
class present in the dataset .The result obtained will be
the face that is to be detected. It was noted that PCA has
better compact detection rates and less processing time
as compared to PCA AND LDA combined work.
Figure1. Images of distortion due to pose, expression,
noise , illumination.
Keqing Shi, Shurong Pang and Fengqi Yu[25]
proposed a face detection technique using PCA, Haarlike features and database. Linear classifier are used to
identify the Haar-like features so that edge contour can
be detected. The Haar-like features are the rectangular
matrix features. Then PCA technique was used to make
the system efficient. By using the Haar-like features the
high false face-like image can be detected .The merit of
this technique is that the face-like image is detected
along with the human body. The drawback of this
method is that it takes much time as both face and
human body is also detected thus increasing the time
complexity.
Yao-Jiunn Chen and Yen-Chun Lin[23]proposed a
face detection technique using by using the geometric
correlation between locations of faces and hairs. They
used ranges of skin color so that location of face region
can be determined .They detected the hairs in the image
by means of hair color spectrum. Thus combining the
two above steps of detecting hairs and skin color of the
faces, the face can be detected .It was observed that the
face detection using this technique acquired 92%
accuracy in detecting a single face in presence of
sufficient light source. The limitation of this technique
is that it can be good in single face detection but multifaces detection performance is not much good
Fig.4 Flowchart of single human face detection.
Fig 5. Face detection and recognition schematic
flowchart.
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International Journal on Advanced Computer Theory and Engineering (IJACTE)
Shiping Zhu and Nan Zhang [26] introduced a
method of face detection in video sequences .They used
the skin color and gradient features to detect the face in
the image. Color images is taken as the input in the skin
segmentation method, Then the Skin color likelihood is
calculated. Thirdly the image is calculated normalize to
get the normal image free from noise Thereafter the
image is turn to gray images .The fit Threshold is used
to make the segmentation of the images. finally getting
the image in the binary form. This method include
features detection of eyes and lip. The skin color
segmentation includes The figure.5 show that flowchart
of Skin Color Segmentation Flowchart. The results of
this method was it gave accuracy in detecting the front
faces. While the face with noise and tilted or illuminated
images were difficult to detect.
III. PROPOSED WORK
In this paper we have tried to merge two techniques
such as PCA and ANN for efficient face detection work.
The proposed system consists of three parts.
1. Feature extraction of image database using PCA
2. Training of ANN for the reduced image subspace.
3. Detection of face for a given input image
A. Image Preprocessing :- Images are required to
preprocess before going for face detection. It includes
filtering of noise from the images, image contrast
normalization and orientation localization. After this
step of processing, image database is prepared for
feature extraction.
B. Feature extraction using PCA:-The PCA algorithm is
implemented for the extraction of image feature and
dimensionality reduction. Let X = {I1, I2,…..IN} be the
set of N images. Each of these images is having
dimension equals to t. In PCA this image space is
converted to another image space with a set Y= { J1,
J2,…..JN} of N images. Now, the dimension of each of
these images is f. There is a transformation of X into Y
such that t > f. Generally, a linear orthogonal transform
v = Wu is used such that the retained variance is
maximized.
C. Multi-Layer Perceptron (MLP) Network :-We have
used multilayer perceptron network for training and face
detection .The network consist of input layer, hidden
layer and output layer with tensing transfer function
.The neural network learns the face patterns from the
training data set and applies it for detecting face object
from the query image.
Fig. 2 Architecture of proposed face detection
Algorithm.
Fig .5. Skin Color Segmentaion Flowchart
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IV. CONCLUSION
Thus several face detection techniques with their
merits and demerits have been studied .Further study
and implementation of new efficient face detection
technique is proposed to overcome the limitation of the
existing algorithm.
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