Improving face recognition by artificial neural network using principal component analysis

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol. 18, No. 6, December 2020, pp. 3357~3364
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v18i6.16335  3357
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Improving face recognition by artificial neural network using
principal component analysis
Shatha A. Baker1
, Hesham Hashim Mohammed2
, Hanan A. Aldabagh3
1,2
Department of Electrical and Computer Engineering, Northern Technical University, Iraq
3
The General Directorate of Education in Nineveh Governorate, Iraq
Article Info ABSTRACT
Article history:
Received Apr 13, 2020
Revised Apr 29, 2020
Accepted Jun 25, 2020
The face-recognition system is among the most effective pattern recognition
and image analysis techniques. This technique has met great attention from
academic and industrial fields because of its extensive use in detecting the
identity of individuals for monitoring systems, security and many other
practical fields. In this paper, an effective method of face recognition was
proposed. Ten person's faces images were selected from ORL dataset, for
each person (42) image with total of (420) images as dataset. Features are
extracted using principle component analysis PCA to reduce the
dimensionality of the face images. Four models where created, the first one
was trained using feed forward back propagation learning (FFBBL) with 40
features, the second was trained using 50 features with FFBBL, the third and
fourth models were trained using the same features but using Elman neural
network. For each person (24) image used as training set for the neural
networks, while the remaining images used as testing set. The results showed
that the proposed method was effective and highly accurate. FFBBL give
accuracy of (98.33, 98.80) with (40, 50) features respectively, while Elman
gives (98.33, 95.14) for with (40, 50) features respectively.
Keywords:
Artificial neural network
Pattern recognition
Principle component analysis
This is an open access article under the CC BY-SA license.
Corresponding Author:
Hesham Hashim Mohammed,
Electronic Computer Center,
Northern Technical University,
University High Way Street, Mosul, Iraq.
Email: hhmss888@gmail.com
1. INTRODUCTION
The technique of pattern recognition is one of the most successful techniques in the field of image
processing. Recently, this technology has been used for security purposes, and has become compatible with security
systems based on other biometrics such as fingerprints and iris [1, 2]. The neural network-based tree [3], neural
networks, artificial neural networks [4] and key component analysis [5] are the common methods used for face
recognition. Principle component analysis (PCA) is a statistical measurement method that reduces the large
dimensions of the image in the data space to smaller dimensions. The new spaces are often feature spaces, this helps
to reduce the calculations of the image database in a controlled way to obtain higher recognition and best accuracy
[6-8]. PCA can be used for extracting the features from a human face intensity image. The basic face database is
defined as all training patterns of the same size and configuration. In this work we utilized facial recognition from
the ORL database. Neural networks gave high efficiency in computational techniques to pattern recognition and
image processing, which led to their widely use in this field [9, 10]. In this research, two types of neural networks
were used: a feedback network and the current frequency network (Elman), both networks gave high accuracy.

 ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 18, No. 6, December 2020: 3357 - 3364
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2. RELATED WORKS
The researchers introduce a reliable and automated system that depend on using multi-algorithms
for face recognition and it's applied on students faces, the system propose using both principal component
analysis (PCA) as well as histogram of oriented gradient (HOG) for feature extraction and artificial neural
network (ANN) for recognition, coupled with help vector machine (SVM). The accuracy of system
recognition was 96.8% [11]. The researchers in [12] presents a Face recognition system in which principal
component analysis (PCA) and back propagation neural networks (BPNN) is performed where used for face
identification and verification while they implement face recognition system is done by using neural network,
Its acceptance ratio was greater than 90% for the proposed matching methods.
In [13], the researchers introduce a face recognition system which use PCA for dimensionality
reduction and feature extraction, with SVM were used as classifier. The system accuracy was 96.8%. In 2013,
other researchers proposed face recognition paper using wavelet transform and PCA with neural network back
propagation. Wavelet transformations were used to calculate the level of image decomposition. Using three levels
the image was disassembled. The study showed the third level is the best level of disassembly [14]. In the other
work [15], a descriptor is obtained by projecting a face as an input on a eigenface space, then the descriptor is
fed as an input to each object's pre-trained network. They determine and report the maximum output if it
passes the threshold previously established for the recognition system.
3. RESEARCH METHOD
Some particular methodologies need to be followed in order to implement the proposed algorithm
dealing with the face recognition system. For this process, certain steps need to be taken. The proposed
algorithm included major steps are as follows, shown in Figure 1.
a. Preprocessing: In this step, the face detection process checks if the image is a face image or not. The face
cropping only from the entire image using the Viola-Jones algorithm [16]. Transform the image of the
face cropped to size (50×60), as shown in Figure 2.
b. Finding the Eigen Data Matrix to be used in subsequent steps to extract feature [17], Figure 3 represents
the flowchart for the finding of the Eigen matrix.
c. Extracting the features of the faces using the Eigen vectors of the faces "calculated by step 2", Figure 4
represent the flowchart of the extraction features process.
d. Training and testing of two artificial neural networks. One of them is of type FFBBL and the other is
Elman Neural network.
Figure 1. Block diagram of the system
Figure 2. steps of Preprocessing

TELKOMNIKA Telecommun Comput El Control 
Improving face recognition by artificial neural network using… (Shatha A. Baker)
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Figure 3. System flowchart of calculating Eigen matrix
Figure 4. System flowchart for finding face features using Eigen matrix
3.1. Back propagation network
BBN has proved successful in identifying and recognition patterns [18, 19]. It is a kind of supervised
learning network. The basis of its work depends on the gradual regression method used to update the weight values
to find the lowest mean square error between the output values of the network and the target values. Therefore,

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the updated weights are used in each layer of the network to begin with the output layer and end with the input
layer, so it is called the back propagation [20], and Figure 5 illustrates the architecture of this network.
The training of the back Propagation network goes through two phases [21]:
 Feed forward stage: The input from the input layer moves to the hidden layer(s) according to their
number, ending with the output layer, and then comparing the actual output values with the desired
output values to calculate the difference between them, which represents the error value.
 Feedback phase: At this stage, the weights of the grid are adjusted depending on the error value. This
process continues until the true output value becomes as close as possible to the desired output value.
Figure 5. Architecture of FFBBL
3.2. Elman network
The Elman network is a special kind of back propagation networks. It is one of the most famous
iterative networks. It has been used in the fields of recognition and classification to contain a dynamic
memory that can be used to retrieve the hidden layer to the input layer, thus speeding up the network.
The Elman network consists of the input and output layers in addition to the hidden layer. It has processing
units equal to the number of processing units in the hidden layer. These units serve as memory to store
the previous state [22, 23].
At the beginning of the training, the processing units are given zero values and the corresponding
weights are single values then adjust during training. The previous values are stored for the hidden layer to be
re-entered by the input layer after the weights have been adjusted. The insertion of the hidden layer is the
result of the input values multiplied by the weight after adding bias values. Using the back-propagation
frequency of the error, the weights are adjusted and the network is given the ability to recognition patterns
through different iterative phases and Figure 6 illustrates the architecture of Elman network [24].
The proposed algorithm used two sets of features 40 and 50 respectivly. These features are extracted
using the eigenface algorithm illustrated in Figure 2. Each set of features are used to Train two types of
artificial neural networks feed forward back probagation learning and Elman neural network. These networks
was trained using the Levenberg-Marquardt Back propagation algorithm for back probagation learning [25].
Figure 6. Architecture of Elman network

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4. RESULTS AND ANALYSIS
4.1. Artificial neural network of feed forward back propagation learning
4.1.1. FFBBL trained with 40 features
The input layer in this neural network has 40 nodes and the output layer has 10 nodes, Figure 7 (a)
illustrates the topology of this network. The training process took 35 minutes 33 seconds and the neural
network reached stability after 21 Epoch. This network accuracy is 98.33%. Network performance was
measured using MSE and it's illustrated in Figure 7 (b).
(a)
(b)
Figure 7. FFBBL artificial neural network trained with 40 features; (a) network topology and
(b) performance curve
4.1.2. FFBBL trained with 50 features
In the input layer 50 nodes of this neural network and output layer 10 nodes, Figure 8 (a) illustrates
the topology of this network. The training process took 24 minutes and 55 seconds to reach a state of stability
after 30 Epoch. This network recognized faces with 98.80% accuracy. Network performance was measured
using MSE and it's illustrated in Figure 8 (b).
(a)
(b)
Figure 8. FFBBL artificial neural network trained with 50 features; (a) network topology and

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4.2. Elman artificial neural network
4.2.1. The network trained on 40 features
In the input layer 40 nodes of this neural network and output layer 10 nodes, the topology of this network
is shown in Figure 9 (a). The training process took 35 minutes and 33 seconds to reach a state of stability after 41
Epoch. This network recognized the faces by 98.33% correctly, Figure 9 (b) shows the MSE performance curve.
(a)
(b)
Figure 9. Elman artificial neural network trained with 40 features; (a) network topology and
4.2.2. Network trained on 50 properties
The input layer has 50 nodes and 10 nodes in the output layer, Figure 10 (a) illustrates the topology
of this network. The training process took 42 minutes 44 seconds and reached stability after 25 Epoch. This
network recognized faces with 95.14% accuracy, and Figure 10 (b) shows the MSE curve. The accuracy of
face recognition of the proposed method is very high. FFBBL has (98.33, 98.80) accuracy, while Elman has
(98.33, 95.14) accuracy for (40, 50) features, respectively. The Table 1 summarize the 4 modules and their
related results information.
(a)
(b)
Figure 10. Elman artificial neural network trained with 50 features; (a) network topology and

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Table 1. Final results summary
Network type Number of features Number of epochs Training time Min: Sec Accuracy
FFBBL
40 21 35:33 98.33%
50 30 24:55 98.80%
Elman
40 41 35:33 98.33%
50 25 42:44 95.14%
5. CONCLOSION
Face recognition is among the most common applications in many areas which can be used as
a replacement to a password or for identification of criminals. In this work a proposed method for Face recognition
by ANN Using PCA was presented. We can infer from the result that there is an inverse relation between both the
number of features extracted and the number of Epochs that is when the features increase the Epochs number
decrease. In either case the recognition accuracy is high. In FFBBL with 50 features, the recognition accuracy is
high comparing with Elman with the same number of features, also the training time increases from 24:55 to 42:44.
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BIOGRAPHIES OF AUTHORS
Mr. Hesham Hashim, received his BSc and MSc degree from University of Mosul-Iraq,
College of computer science and mathematics, Computer Science department, at 2010, 2013
respectively. He worked as assistant lecturer in computer science department, college of
computer science and mathematics, mosul, Iraq for the period 2013 to the end of 2016. Since
2017 he worked as assistant lecturer in Northern Technical University (NTU), Mosul, Iraq.
He is now a Ph. D student at the computer science and mathematics college. His interested
research area includes AI applications, digital image processing and bioinformatics.
Mrs. Shatha obtained a BSc in Computer Science from the University of Mosul in 1997,
and in 2013, she earned an MSc in Computer Science from the same University. She worked
as a lecturer in Northern Technical University (NTU), Mosul, Iraq. Her interested research
area includes Mobile programming, Information Security and Multimedia communications
and Artificial intelligence. She is now a Ph. D student at the computer science and
mathematics college.
Ms. Hanan, in 2008 received her BSc in Computer Science from the University of Mosul,
and in 2014 she earned an MSc in Computer Science from the same University. She works
as an employee The General Directorate of Education in Nineveh Governorate, Iraq. Her
interested research area includes wireless network, Information Security and Multimedia
communications and Artificial intelligence. She is now a Ph. D student at the computer
science and mathematics college.

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