Measuring memetic algorithm performance on image fingerprints dataset

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol. 19, No. 1, February 2021, pp. 96~104
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v19i1.16418  96
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Measuring memetic algorithm performance on image
fingerprints dataset
Priati Assiroj1
, H. L. H. S. Warnars2
, E. Abdurrachman3
, A. I. Kistijantoro4
, A. Doucet5
1,2,3
Computer Science Department Binus Graduate Program, Bina Nusantara University Jakarta, Indonesia
1
Information System Department, Universitas Buana Perjuangan Karawang, Indonesia
1
Politeknik Imigrasi, Ministry of Law and Human Right, Republic of Indonesia, Indonesia
4
School of Electronics Engineering and Informatics, Institut Teknologi Bandung, Indonesia
5
L3i Laboratory, La Rochelle University, France
Article Info ABSTRACT
Article history:
Received Apr 17, 2020
Revised Jul 2, 2020
Accepted Aug 29, 2020
Personal identification has become one of the most important terms in our
society regarding access control, crime and forensic identification, banking
and also computer system. The fingerprint is the most used biometric feature
caused by its unique, universality and stability. The fingerprint is widely used
as a security feature for forensic recognition, building access, automatic teller
machine (ATM) authentication or payment. Fingerprint recognition could be
grouped in two various forms, verification and identification. Verification
compares one on one fingerprint data. Identification is matching input
fingerprint with data that saved in the database. In this paper, we measure the
performance of the memetic algorithm to process the image fingerprints
dataset. Before we run this algorithm, we divide our fingerprints into four
groups according to its characteristics and make 15 specimens of data, do four
partial tests and at the last of work we measure all computation time.
Keywords:
Biometrics
Fingerprints
Image
Memetic algorithm
Performance
This is an open access article under the CC BY-SA license.
Corresponding Author:
Priati Assiroj
Computer Science Department Binus Graduate Program
Bina Nusantara University
Jl. Raya Kebonjeruk, Anggrek, DKI Jakarta, Indonesia
Email: priati@binus.ac.id
1. INTRODUCTION
The increase of digital crime, such as data manipulation and image and signature falsification, and
many various illegal transactions, is equal to the request of its solutions. Computer-based biometrics validations
are important tools to improve the system's security. The most important of biometrics are measure physiology
and behavior characteristics that allow making authentication to personal identity. Biometric authentication in
the computer-based application is an important thing caused by the huge of sensitive data in the computer
system and it increases rapidly.
Personal identification has become one of the most important terms in our society regarding access
control, crime and forensic identification, banking, and also computer system [1]. Biometric features that can
be used to identification include the iris, voice, DNA, and fingerprints. According to [2], the fingerprint is the
most used biometric feature caused by its unique, universality, and stability. The fingerprint is widely used as
a security feature for forensic recognition, building access, automatic teller machine (ATM) authentication, or
payment. Automatic fingerprint identification has become an interesting research topic for two decades [3], in
accordance with [4] fingerprint because of its unique, size, and peculiarity. Nowadays, to get a fingerprint

TELKOMNIKA Telecommun Comput El Control 
Measuring memetic algorithm performance on image fingerprints dataset... (Priati Assiroj)
97
recognition tool is very easy, many corporations and organizations use it to identify their member [1].
Fingerprint recognition could be grouped into two various forms, verification [5] and identification [6].
Verification compares one on one fingerprint data. Identification is matching input fingerprint with data that
saved in the database. Therefore, identification is an extension of verification that compare one fingerprint data
to many, and note that identification is more complex than verification.
2. RESEARCH METHOD
2.1. Memetic algorithm
Memetic algorithm (MA) is an algorithm based on the Neo-Darwinian Evolution concept and
Dawkins' opinion about memes as cultural evolution unit which can make improvements to itself. MA is a
heuristic method which has similar characteristic to genetic algorithm (GA), combined with local search
method, that can improve quality solutions [7]. On MA, local search is used for local improvement which can
be applied before or after selection, crossover, and mutation process. Local search is also very useful to control
search space. MA can achieve a better result than GA but need more computational time.
MA is a simple algorithm but flexible and powerful [8, 9], that can find quality solutions in many
challenging problems [10-12]. Optimization problems will involve dozens of variables thus need good coding
devices to overcome computational time. This algorithm will very useful in data mining process such as
agglomeration and text analysis [13, 14], biomedical, such as DNA and molecular simulation [15-17], network
problems [18, 19], feature selection technique [20], molecular simulation [16], forecasting [21], quantum
chemistry [22], spectroscopy analysis [23], geophysics analysis [24], medicine invention [25], genome
study [26], and many more.
Specifically, the use of algorithms for image processing has been done by [27], they have used
memetic algorithm for image brightness enhancement; [28] use this algorithm to detect face emotion,
while [29] use this algorithm for letter recognition. Handwritten recognition conducted by [30] and [31], retina
recognition by [32], sub-pixel mapping imagery by [33], image classification by [34], and fingerprint matching
with the memetic algorithm by [35], [36] and [5].
2.2. Biometrics
Biometrics are special characteristics of human which is unique on every individual and can be a
reference of identification and verification. Biometrics characteristics can be categorized into two types,
physical and behavior. We all know that physical types such as fingerprint, iris, retina, and face, also behavior
type such as voice, signature, gait, palm geometry, handwritten, electrocardiograph (ECG) [35]. The fingerprint
is one of the most reliable biometrics features caused by its unique, and it is impossible to find the same
fingerprint between two different people. This biometrics feature can automatically recognize someone by
pattern recognition and determine authenticity physiology characteristics.
2.2. Fingerprints
The fingerprint is a graphic ridge and valley pattern at the tip of a human finger. Human fingerprints
can be found on many historical objects as shown in Figure 1. This finding proves that ancient people were
aware and gave special attention to fingerprint individuality, despite they did not have a scientific basis [37].
The history of fingerprint begins in 1684, morphologist from England, Nehemiah Grew, published his scientific
paper about the ridge, groove, and pore structure of fingerprint [37]. In 1788 Mayer has described fingerprint
in detail [38].
(a) (b) (c) (d)
Figure 1. Fingerprints on historical objects, (a) neolithic sculpture [38], (b) stonehenge at Goat Island [37],
(c) clay cup from China 300 BC [37], (d) traces on the light of Palestinian 400 AD [38]

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TELKOMNIKA Telecommun Comput El Control, Vol. 19, No. 1, February 2021: 96 - 104
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Thomas Bewick in 1809 uses the fingerprint as its characters and it is believed as the milestone of
scientific fingerprint [38]. In 1823, Purkinje classifies fingerprint as the first. He was classified fingerprint to
nine categories based on the ridge configuration, and in 1880 Henry Fauld and Herschel have done scientific
fingerprint recognition [38]. This invention is the basis of modern fingerprint recognition. At the end of the
nineteenth century, Sir Francis Galton researched more about fingerprints. Galton introduces a small feature
for fingerprint matching in 1888. The most important progress in fingerprint recognition occurred in 1899 when
Edward Henry generates "Henry System" [37]. So at the beginning of the twentieth century, the form of
fingerprints can be understood well.
3. IMPLEMENTATION MODEL
There are several processes conducted to implement this algorithm. We start the process with reading
folders and fingerprint files, we will do the local search process. Then convert fingerprint files to string array
form and convert string array to binary code form with Base64, the process will be looped until the entire data
converted successfully. The next process is selection or elitism for parent candidates from the total population
then make it crossing-over to get new offspring. The last process is a mutation or clones the offspring.
Figure 2 shows the flowchart of the process of the memetic algorithm, and a detailed process will be described
in the next paragraph. In this paper we implement MA on 7200 synthetic image fingerprint dataset of FVC2006
with characteristics as shown in Table 1.
Figure 2. Flowchart of memetic algorithm
Table 1. Groups of fingerprint image data
Groups Contents
A 100% fully-sized fingerprints image
B 60% fingerprints with dark color boundaries
C 60% fingerprints with bright color boundaries
D 80% fingerprints with bright color boundaries and unclear image

99
Then we conduct several steps as described below:
− Local search. We start the local search by reading the folder and fingerprint file h identification and data
processing. We divide image fingerprint data into four groups, as shown in Table 1, based on its
characteristics.
− Conversion. In this step is convert fingerprints image data to string array then convert string array to binary
code use Base64. In this algorithm, we will do some swap, crossover, and mutation process, inside there,
are crossing and data cloning that needs binary input and output, so we need it to be converted to binary
code. We present the code in java language to convert string array to binary code as shown in Figure 3.
− Elitism. In this step, we conduct some selection steps. Theoretically, in elitism, we need a 20% random
sampling of the population. In the preliminary experiment, we only use a 2% random sampling of the
population caused by the limitation of our hard drive. Figure 4 shows the elitism code with java language.
At the end of code, we finish with "null" to clear variables that are not used so that RAM is not full.
− Crossover. Crossover is a hybridization step of every selected sample to get new offspring. The selected
samples divide into two groups, male and female, then they will be swapped one to the whole selected
sample in another group, this process will be looped until the last selected sample. Figure 5 is the illustration
of the crossover process in this paper and Figure 6 shown crossover code in java language.
− Mutation. This is the last process, where new offspring from the crossover process cloned then reversed
several binary lines to get new offspring. Figure 7 shows the code of the mutation process in java language.
All steps were coded in java language with Netbeans IDE 8.2 and Experiments carried out use host and guest
operating system in 1 PC with VMBox. This system runs in Intel i5-2540M with 16 GB of memory.
Figure 3. Read file or folder and conversion code in Java
This work consists of several steps. At the first step we conduct some steps as describe below:
- Read fingerprints image file in every folder and we read one by one.
- Convert image data to array string type using Base64.
- Convert array string to binary code. This is a must because we use binary type in the swap or crossover
process and mutation process.

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- Population filtering to select parent candidates. In the first experiment, we use 20% of the population then
we got swap or crossover result was too big. Caused by the limitation of our hard drive, then we decided to
use only 2% of the population so that the swap results will not overwhelm the hard drive.
- Swap or crossover process, mating all of the 2% of the population to get the new offspring. The process is
to mate every binary to each other then we got 4900 new offsprings.
- The last process is the mutation for 4900 new offsprings from the crossover process. This is a binary reverse
process where each value of 1 will be reversed to 0 and 0 will be reversed to 1.
These codes above are to read every folder that contains fingerprints image data, then converted to
array string type using the Base64 process and the result then converted to binary code because crossover and
mutation process is mate and clone process of binary code. Figure 4 is the elitism code. Select randomly from
20% of the population to find parent candidates and because of the limitation of our hard drive we use only 2%
of the population. The last “null” code is to empty the variable when it is not used so that RAM is not full.
Figure 5 is a crossover function code to mate every selected sample to get the new offsprings where
the selected sample divided into 2 groups, male and female then swap to all samples to the other group and
looped it. The swap process is depicted in Figure 6 starting from sample 1 in one group mated with all samples
in the other group. Likewise with sample 2 and others. This process will be repeated continuously until all the
samples have been successfully mated. The last process is the process of mutation. The program code is in
Figure 7. Where the results of the marriage from the crossover process are cloned to then reverse the binary
code, the value of 1 becomes 0 and vice versa, to get new offspring.
Figure 4. Elitism code in Java
Figure 5. Crossover code in Java language

101
Figure 6. Crossover illustration
Figure 7. Mutation code in Java
4. EXPERIMENTAL RESULT
According to Table 2, experiments carried out with 15 specimens of data. Once specimen consists of
fingerprint data that grouped before. The following is a description of each specimen;
Specimen 1 consist of the entire fingerprint data
Specimen 2 consists of fingerprint data type A
Specimen 3 consists of fingerprint data type B
Specimen 4 consists of fingerprint data type C
Specimen 5 consists of fingerprint data type D
Specimen 6 consists of a combination of fingerprint data type A to type B
Specimen 7 consists of a combination of fingerprint data type A to type C
Specimen 8 consists of a combination of fingerprint data type A to type D
Specimen 9 consists of a combination of fingerprint data type B to type C
Specimen 10 consists of a combination of fingerprint data type B to type D
Specimen 11 consists of a combination of fingerprint data type C to type D
Specimen 12 consists of a combination of fingerprint data type A, B, and C
Specimen 13 consists of a combination of fingerprint data type A, B, and D
Specimen 14 consists of a combination of fingerprint data type B, C, and D
Specimen 15 consists of a combination of fingerprint data type A, C, and D
According to these specimens, we conduct four steps of experiment, consists of:

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- Partial Test 1. In this part, we start with reading fingerprint image data, convert data to array string, and
convert array string to binary code.
- Partial Test 2. This is a selection process which uses elitism principal. We select parent candidates 2% of
the entire population.
- Partial Test 3. This part is a swap or crossover process to generate new offspring.
- Partial Test 4. This is the last where we do mutation to each offspring.
We measure every processing time in millisecond for each partial test then add them up. Following
the results test specimen, 2 achieved the best result and has the most efficient processing time with 109168 ms.
The specimen 1 is the worst compared to the entire specimen with total processing time 4032220 ms. This
algorithm has processed every specimen successfully with an average of total processing time 1516986 ms.
Table 2. Numerical experiment result
MA Result Data
Data
Storage
Partial Test (ms) Total
Time
Original Elitism Swap Mutation
1 2 3 4
Specimen 1 710548 1972509 561290 787872 4032220 7200 140 4900 4900 22.8 GB
2 68180 17167 6323 17498 109168 1800 35 289 289 270 MB
3 241623 212153 43042 63946 560765 1800 35 289 289 4.8 GB
4 256843 215656 45350 70968 588818 1800 35 289 289 4.3 GB
5 171731 127978 27244 44162 371115 1800 35 289 289 2.4 GB
6 301021 472539 207748 339992 1321301 3600 70 1225 1225 8.9 GB
7 259104 363373 158062 279576 1060116 3600 70 1225 1225 7.9 GB
8 211430 261118 115808 212052 800408 3600 70 1225 1225 4.5 GB
9 469325 745542 159725 245119 1619711 3600 70 1225 1225 11.4 GB
10 499744 646664 110142 176031 1432582 3600 70 1225 1225 8 GB
11 390723 587601 109648 175632 1263607 3600 70 1225 1225 7.6 GB
12 597502 1228740 416651 684167 2927060 5400 105 2704 2704 17.4 GB
13 485254 1002529 307545 450989 2246318 5400 105 2704 2704 13.1 GB
14 620749 1277471 244341 377135 2519696 5400 105 2704 2704 15.8 GB
15 355975 805250 264398 476298 1901923 5400 105 2704 2704 12.3 GB
5. CONCLUSION
In this paper, we divide the image fingerprint data into 15 specimens to do four partial tests. In order
to know the performance of MA, we measure every specimen in every partial test then generate each processing
time. According to the results, specimen 2 achieves the best processing time with 1019168 ms, it has the most
efficient processing time of all specimens. At the feature, we will conduct some experiments to this algorithm
performance in many environmental systems, such as in a computer network.
ACKNOWLEDGEMENTS
This work is supported by Research and Technology Transfer Office, Bina Nusantara University as a
part of Bina Nusantara University’s International Research Grant entitled MEMETIC ALGORITHM IN
HIGH-PERFORMANCE COMPUTATION with contract number: No.026/VR.RTT/IV/2020 and contract
date: 6 April 2020.
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BIOGRAPHIES OF AUTHORS
Priati Assiroj, was born in Cirebon, Jawa Barat, Indonesia in 1986. She has Bachelor and
Master in Computer Science. She received the Bachelor from STMIK Bani Saleh Bekasi, in
2011and received her Master from STMIK LIKMI, Bandung, Indonesia, in 2016. From 2014
to 2016, she was a lecturer in Universitas Singaperbangsa Karawang, Indonesia and from
2016 and recent she is a lecturer in Universitas Buana Perjuangan Karawang in Information
System Dept. Since January 2019 she is a lecturer in Politeknik Imigrasi, Ministry of Law
and Human Right, Republic of Indonesia. Since March 2018 she has been a scholar of Bina
Nusantara Graduate Program, Doctor of Computer Science, Bina Nunsantara University
Jakarta, Indonesia. Her research fields are data mining, high performance computing and
evolutionary algorithm.
H. L. H. S. Warnars, received a Ph.D. degree in Computer Science from Manchester
Metropolitan University. Since September 2015 he is a Head of Information Systems
concentration at department Doctor of Computer Science Bina Nusantara University, works
some project research with my doctoral computer Science students in research area such as
Game, Artificial Intelligence including Data Mining, Machine Learning and Decision
Support System application such as DSS, BI, Dashboard, Data Warehouse, and so on
Edi Abdurrachman, received B. Sc and Master of Statistics in Applied Statistics from
Bogor Agricultural University, then received M.Sc and Ph.D. in survey statistics and
statistics from IOWA State University, USA. He is currently a professor and dean of the
Binus Graduate Program, Doctor of Computer Science, Bina Nusantara University Jakarta.
His research interest includes statistics, survey statistics, and applied statistics and
management information systems. Mr. Abdurrachman’s awards and honors include the MU
SIGMA RHO Society (1985) and Best Lecturer Binus University (2012). He is also a
member of the American Statistical Association, International Association of Engineers
(IAENG), Gamma Sigma Beta, and as a Vice President of the Asian Federation for
Information Technology in Agriculture. From 1980-2015 actives in the ministry of
agriculture in many positions of director. He is also active as a public speaker in national
and international seminars.
Achmad I. Kistijantoro, received the B.Eng. degree in informatics from the Institute of
Technology Bandung, (ITB), Bandung, Indonesia, the masters’ degree from TU Delft, Delft,
The Netherlands, and the Ph.D. degree from the University of Newcastle upon Tyne,
Newcastle upon Tyne, U.K., His current research interests includes distributed systems,
parallel computation, and high-performance computation.
Antoine Doucet is a Full Professor in computer science at the L3i laboratory of the
University of La Rochelle since 2014. He leads the research group in document analysis,
digital contents and images (about 40 people) and is additionally the director of the ICT
department of the Vietnam-France University of Science and Technology of Hanoi.
Additionally, he is the principal investigator of the H2020 project NewsEye, running until
2021 and focusing on augmenting access to historical newspapers, across domains and
languages. He further leads the effort on semantic enrichment for low-resourced languages
in the context of the H2020 project Embeddia. His main research interests lie in the fields of
information retrieval, natural language processing and (text) data mining. The central focus
of his work is on the development of methods that scale to very large document collections
and that do not require prior knowledge of the data, hence that are robust to noise (e.g
stemming from OCR) and language-independent. Antoine Doucet holds a PhD in computer
science from the University in Helsinki (Finland) since 2005, and a French research
supervision habilitation (HDR) since 2012.

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