A novel multi-biometric technique for verification of secure e-document

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 1, February 2024, pp. 662~671
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp662-671  662
Journal homepage: http://ijece.iaescore.com
A novel multi-biometric technique for verification of secure
e-document
Ammar Mohammed Ali1
, Alaa Kadhim Farhan2
1
Chemical Engineering Department, University of Technology, Baghdad, Iraq
2
Computer Sciences Department, University of Technology, Baghdad, Iraq
Article Info ABSTRACT
Article history:
Received Jul 12, 2021
Revised Oct 5, 2023
Accepted Oct 6, 2023
Extracting unique and distinctive traits is one of the most important
challenges that researchers face, who rely on biometrics to extract
exceptional traits for an individual. A large amount of biometric evidence
that can be identified and found in various research has been done. In this
paper, a biometrics system is proposed that combines the benefits of
fingerprinting and uses a novel strategy to combine it with the image-based
fingerprint vein feature set. The proposed system is fast and performs
effective personal identification by combining both features. The features
extracted from the venous print and fingerprint are matched to the nearest
neighbors of the authorized person forms to verify the identity of the person.
Several experiments have been performed on selected datasets to evaluate
the performance of the new biometrics system. The obtained results prove
that our proposed system is superior to biometric systems that use the feature
of single biometrics. However, our goal is to set up an algorithm that is
inexpensive in terms of time complexity while keeping it at the required
security levels.
Keywords:
Biometric technologies
Fingerprint
Security
Time
Verify the identity
This is an open access article under the CC BY-SA license.
Corresponding Author:
Alaa Khadim Farhan
Computer Sciences Department, University of Technology
Baghdad-10066, Iraq
Email: 110030@uotechnology.edu.iq
1. INTRODUCTION
Biometric technologies usually refer to the use of innovation and research on an individual
dependent on part of his knowledge. Recognition of the unique mark is one of the individual and unique
biometric developments that were freely gathered under computerized criminology. The verification process,
in general, can serve as a review, examination, and test to ensure the strength of the system. Manual
verification suffers from many problems. It is old, inefficient methods, high cost, and lack of experience in
error diagnosis steps. It takes a long time because it depends on the human. Discovering the current state of
uses and studies on documenting and verifying electronic documents and electronic archives has become one
of the most important things to transfer in this world. They occasionally adapt to various reports, confessions,
licenses, and other records that secure more than the legitimacy of need, while others secure documents that
are safer than lack of authentication [1], [2].
As a result of a large degree of continuous use of electronic documents, it caused the introduction of
information technology in our daily lives for specific mobile devices and various social networks. There are
many methods used to verify and authenticate, as we must think about modern electronic methods that do not
depend in the verification process on the authenticity of documents, and human experience [3]–[5].
Technologies will significantly outperform deliver accuracy and save time where validation depends on the
parameters extracted from the digital signature solution prepared for this purpose, based on several

Int J Elec & Comp Eng ISSN: 2088-8708 
A novel multi-biometric technique for verification of secure e-document (Ammar Mohammed Ali)
663
techniques used to process different types of images. The main objective of this research paper is to reduce
cases of forgery and tampering in electronic documents. Many researchers extracted different biometric
features from finger veins and fingerprints such as [6]–[8]. Methods using biometric devices have been
developed and optimized to simulate a high-safety feature. To achieve this goal, some researchers resorted to
developing multimedia biometric systems. Individual biometric systems differ in many types, including
fingerprints, finger veins, hands, iris, and amplifiers. Regardless of all the traditional methods of
authentication studies, biometrics systems have emerged and have played an effective role in many areas,
especially in industry and in various management matters. The different methods and techniques have been
used to implement the system [9]–[11]. The proposed system observed respect for the issue of validation and
verification, which are explained in detail in the rest of the paper.
2. SECURITY AND AUTHENTICATION SYSTEM
The data varies according to the different types and methods of storing it, including big data and
database and their management [12] or electronic data and documents, and all types of data used in different
systems need to follow different methods of extracting data and dealing with it [13], [14] and adopt the
concept of authentication. Authentication may include approving individual personality records, confirming
the credibility of a site with an electronic certificate [15], [16]. Lee et al. [15] have studied digital signature
standard schemes to explore their potential applications in various fields. The term advanced verification,
otherwise called electronic confirmation or e-validation, alludes to a gathering of procedures where the
certainty for client characters is set up and introduced through electronic strategies to a data framework. The
validation of data can present extraordinary issues with electronic correspondence, for example, weakness to
man-in-the-center assaults, whereby an outsider takes advantage of the correspondence stream, and stances as
every one of the two other imparting parties, to block data from each. Additional personality variables can be
required to verify each gathering’s character [17], [18].
3. PREVIOUS WORKS
Document validation has been carried out by comparing both the signature and the sample that was
relied upon at the time of document verification. The merging of features of each of the fingerprints is
combined with the venous finger, and each of them carries multiple biometrics features, which represent the
traits of multiple traits that may be different and sometimes incompatible with each other, many conditions
and obstacles accompany the use of biometric devices such as hand moisture, placing a weak finger, dirt, and
dust on the screen of the scanner, which takes pictures of the fingerprint, whether the fingerprint or
intravenous fingerprint, these conditions, and others, have helped open the magazine to researchers to
overcome obstacles, through research the best way. Therefore, in this research, a proposal has been made to
suggest a new way to create unique and powerful features that address the weak points and problems of
fingerprint extraction. As a review of the finger vein recognition system, numerous ongoing existing finger
vein acknowledgment frameworks including picture preprocessing, highlight extraction, and arrangement
stage have been investigated. The examination should concentrate on the key issue and the future works that
are talked about in this paper to beat the shortcomings of the past techniques to build up an all the more
remarkable finger vein-based identification or verification or check framework [19].
The palmprint and palm vein images are fused by a new edge-preserving and contrast-enhancing
wavelet fusion method in which the modified multiscale edges of the palmprint and palm vein images are
combined [20]. In [21], a fusion of finger vein (FV) and finger-knuckle-print (FKP) images have been
applied and planned to build up a quick calculation for individual distinguishing proof. Since there is no open
multimodal database of FV and FKP pictures, in this paper a gadget that can catch both FV and FKP pictures
has been arranged to carry out this responsibility.
In the area of secure document authentication, facial recognition techniques were used to
authenticate secure documents based on a two-to-two diode pattern and a directed gradient graph. It offers
benefits for clinical registry purposes, web-based banking services, driver’s licenses, personal identification,
and robotic identity verification-officer controls and law requirements as well. This relies on a strategy based
on facial features such as eyes, nose, and mouth, and they take care of their areas and nearby visions in the
workbook. In this paper, we addressed the problems related to the feature extraction from the palmprint in a
framework related to the biometric authentication of individuals [22]–[24].
4. PROPOSED METHOD
Our use of several additional successful features has had a major impact on increasing the ability to
recognize and verify the system. Therefore, it can be said that the biometric system is used to identify and at

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the same time authenticate. The merging of features of each of the fingerprints is combined with the venous
finger and each of them carries multiple biometrics features, which represent the traits of multiple attributes
that may be different and sometimes incompatible with each other, so it came to our suggested way to create
new features that are capable of to overcome the weaknesses in the features extracted from the fingerprint.
Not so long ago, different types of composite biometrics frameworks were created and synthesized, which
include unique frames for each of the distinctive features of the face, voiceprint, and fingerprint. Regardless
of the traditional verification strategies, isometric frameworks have been effectively developed in various
businesses and management matters, and these methods go forward in giving higher safety points to reach the
control framework. Fingertip vein design is a new and fast biometric feature that has been considered by
many professionals as a promising option for identifiable identification.
The design of biometric veins, such as the human blood vessels of a distinct individual, depends on
how each individual has a unique vein-type design. Since many external biometric traits, for example, a
unique mark, palm imprint, and face accentuation are progressively helpless against spelling and can be
manufactured using moderate endeavors for it, dependence and safety are becoming a major problem against
these discriminatory biometric traits evidence-proof. Likewise, clinical tests have shown that the design of
veins in each individual is unique and stable for an extended period.
4.1. Main structure of proposed algorithm
The document authentication process is based on biometric features. Where the extracting unique
features and described both fingerprints and at the same time it is veins. These properties are documented
on the document in the form of an electronic fingerprint. It is used to get to know the authenticity of the
document later. Accordingly, different arithmetic settings are adopted to process regular and safe
biometric accounts. The computing of this field depends on two parties, the first party represents the basic
database that contains the ideal characteristics of fingerprints and finger veins for authorized persons. The
second part represents the fingerprint on the document, meaning that each party has their entries, and they
will work together to calculate a function and validate the document. Therefore, the advantages and
characteristics of biometric features such as fingerprints and veins are difficult to counterfeit, especially in
the case of merging them just as in our proposed system, unlike ID cards where it is easy to copy and
falsify the signature while it is difficult to copy biometric features. Verifications were made by following
different methods and techniques, which range from following traditional methods through the calculation
of a simple Euclidean distance or by relying on crossbreeding works. In this paper, a system has been
proposed to verify the authenticity of electronic documents by relying on multi-character biometric
features that depend on both the fingerprint and the fingerprint. As a result of the different acquisition
conditions of the biometric devices for taking fingerprint images or finger veins, many obstacles appeared,
such as moisture on the hand, dust on the screen of the open, or even the poor position of the finger,
making the field open for researchers to search for an appropriate method that can overcome all these
obstacles. The merging of features of each of the fingerprints is combined with the venous finger, and each
of them carries multiple biometrics features, which represent the traits of multiple traits that may be
different and sometimes incompatible with each other, so it came to our suggested way to create new
features that are capable of to overcome the weaknesses in the features extracted from the fingerprint. Our
proposed system relies on two main features: The designation circular projection was launched to calculate
the density of the image for which the features were to be extracted While the second type relied on groups
for multi-set features as these two types of features were used in our proposed methods and as will be
explained in the next sections.
− Proposed algorithm 1 (discrete processing): Creating features from separate finger vein and fingerprint
In our proposed multi-biometric system on the method of combining both fingerprints, the scanning
is done through the use of a special scanner whose specifications are shown in this paper, which performs a
scanning process for both the fingerprint and veins at the same time and gives us two different devices. We
can clarify the basic steps that will be explained in the following diagram. The main steps of the proposed
algorithm 1 can be summered as flowing. The first proposed algorithm (discrete processing).
The feature vectors extracted from both the fingerprint and finger vein data are stored in the system
as two separate vectors, denoted as (PPF) for fingerprint features and (VPF) for finger vein features. These
vectors, (PPF, VPF), are designed to be used for verification purposes at a later stage. This verification can
take place either independently, where the fingerprint and finger vein data are checked separately, or in a
combined manner, where both sets of data are considered together to enhance the accuracy of the
authentication process. Figure 1 illustrates this concept further.
− Proposed algorithm 2: Create a novel 67 features as proposed multiset features (discrete processing).
− Proposed algorithm 3 (combine processing): Creating features from finger vein and fingerprint.

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Algorithm 1. Creating features from separate finger vein and fingerprint
Input: Fingerprint and finger vein image
Output: Biometric features
Begin
Step 1: The vein image is extracted from the special biometric scanner.
Step 2: The image is initially processed to improve it.
Step 3: The fingerprint is extracted from the special scanner device.
Step 4: Initially manipulate the image to improve it,
Step 5: The edges of both images (fingerprint and veins) are extracted by applying the sharp edges method using canny edge detection.
Step 6: The intensity of the projection is calculated circularly, through the center of the image, and it is adopted to calculate the
precipitation at all angles at an angle of 360 degrees, to obtain 360 features that represent the density of the image in different
directions, where this method can be called a circular projection.
End
Algorithm 2. Multiset features (discrete processing)
Input: Fingerprint and finger vein image
Begin
Step 1: The first 60 features can be obtained by any method of algorithm1: the 360 features are divided into 60 features. Each of these
features is an average of 6 out of 360 features, meaning that we used to choose a sliding window in the form of a fan blade.
Step 2: Two features from the center of gravity [25] of the first image (p1) the center of gravity concerning the individual (i) can
demonstrate in the following format.
𝑐𝑜𝑔 = 𝑠𝑢𝑚(𝑥 ∗ 𝑦)/𝑠𝑢𝑚(𝑦);
Step 3: Two features from the center of gravity of the second image (p2).
Step 4: Number of intersect between p1 & p2.
Step 5: Calculate the mean of the image: mean(p1), mean(p2); Where xi denotes the pixel of the image
Step 6: Mean(intersect)
End
Algorithm 3. Features from finger vein and fingerprint
Begin
Step 1: The vein image and fingerprints are extracted from a biometric scanner and enhanced these images as we explained in the
previous method.
Step 2: The image of the finger vein and fingerprints is combined to produce a new image that will be dealt with and extract features
from it.
Step 3: The edges of the image are extracted using (canny Edge Detection). And then preprocess the vein images to match the size of the
fingerprint image.
Step 4: The 360-degree circular features are extracted as in the previous method, but the difference in this method is that we applied this
method by extracting the features after combining the two images (the vein image and the fingerprint image). This method can be
called circular projection, which denotes the set of properties of both images, as shown in Figure 2 which illustrates the basic
steps illustrating the block diagram of proposed algorithm 1 (combine processing).
End
Figure 1. The block diagram of the proposed algorithm discrete processing

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Figure 2. The block diagram of the proposed algorithms 1 and 2 (combine processing)
5. EXPERIMENTAL RESULTS AND DISCUSSION
The verifications are made by different methods, which range from traditional methods through the
calculation of a simple Euclidean distance or by relying on crossbreeding works. In this paper, a system has
been proposed to verify the authenticity of electronic documents by relying on multi-character biometric
features that depend on both the fingerprint and the fingerprint. In light of the results inspired by practical
experiences, we can see the great effectiveness of our proposed method by achieving high degrees of
accuracy and distinction as we relied on various methodologies for each approach, and the calculation time is
required to extract properties. This research relied on a versatile scanner device that takes both fingerprint
and vein fingerprints at the same time and stores them as two separate images. This will be addressed later in
this research. We can observe from the practical results, the great superiority of our system by achieving the
highest levels of accuracy and distinction by relying on methodologies that rely on new and different
structures that adopt an approach that avoids complexity while maintaining the time required for calculating
and extracting properties. A very large number of experiments were conducted on the selected data groups to
evaluate the features extracted from our biometric system. In the step of obtaining information, intravenous
images are taken using an infrared scanner. This is another major feature of validating finger vein design by
adopting biometrics. Correct index finger index images were used for individuals to create and evaluate
forms, knowing that our proposed system can rely on any of the fingers of the hand. Moreover, the proposed
framework is organized in a single layer that combines the integration of extracted characteristics with the
preservation of information security. The verification accuracy of the proposed default dataset when using an
expansion procedure by combining the features of a fingerprint with a vein is better than using a dataset that
relies on individual features. There are many problems with the finger, such as the deformation of the skin
surface, which weakens the accuracy of the frame.
5.1. Evaluate proposed different methods to extracted different features vector
Features are extracted from fingerprints and venous fingers in several ways that a comparison has
been made between these methods. We can notice that each of the extracted features depends on the method
and mechanism of work that differs from the other. The extracted features are variable with the different and
changing method, which are the outputs of each road different from the outputs of the second method, in
terms of the number of features extracted, which represents the size of the feature vector.
5.1.1. First way
Features derived from the first method using a multi-biometric principle. Multiple biometric features
were extracted by doing a pre-treatment of the features extracted by the template, by making the size of the
finger vein features the same size as the fingerprint features including performing one of the two operations:
i) Delete from the fingerprint features file if its size is larger from 702 arranged to be the same size and
ii) Add a set of zeros if it is smaller than 702 arranged to be the same size). Then the procedure for dividing
the two features is taken. This requires adding the number 1 to all elements in extracted features, that is, to all
ranks for each feature, to avoid dividing by zero in the next stage. This addition of number one did not affect

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the general distribution structure of the features; that means the relationship (distance) between extracted
features is the same. For example, if the extracted trait is 7 4 3 2 1 then when adding 1 it becomes 8 5 4 3 2
Note that the trip connecting the elements is the same (7-4=8-5) and (4-3=5-4).
The last steps of this method are introduced using a multi-biometric principle: We denote the
fingerprint features as FPF:(FPF+1=FPF1). Where we code FPF1 fingerprint features after adding 1. We
denote the finger vein, characterized by the symbol FVF which means (FVF+1=FVF1) It is characterized by
the symbolization of FVF1 fingerprint vein after adding 1. Multiple biometrics features=FPF1/FVF1.
5.1.2. Second way
Features are extracted from proposed algorithm 1 (discrete processing): creating a novel 360
features from separate finger vein and fingerprint in this research to extract the circular projection features of
fingerprint and venous finger separately. Initial treatment of this is done by adding No. 1 to all elements of
the extracted features. Accordingly, as in the previous method, multiple features will be extracted as multiple
biometrics features=FPF1/FVF1.
5.1.3. Third way
Features are extracted from fingerprints and venous finger separately by applying proposed
algorithm 2 that create a novel 67 features as proposed multi set features (discrete processing) as we
discussed before. Initial processing is done by adding no. 1 to all elements of the extracted features.
Accordingly, as in the previous method, multiple features will be extracted as multiple biometrics
features=FPF1/FVF1.
5.1.4. Fourth way
The paragraph discusses a feature extraction process involving both fingerprints and finger vein
characteristics. It employs algorithm 1, known as “combine processing,” to create a novel set of 360 features
by merging information from finger vein and fingerprint features. The key aspect of this feature extraction
method is the utilization of circular projection features, which are derived from the combination of two
distinct images, namely fingerprint and venous finger images, as previously explained in the text. This
paragraph underscores the innovative nature of this approach, highlighting the integration of two different
biometric data sources to produce a comprehensive set of 360 features, with a particular focus on the circular
projection technique.
5.1.5. Fifth way
Features have extracted both fingerprints and fingering by applying proposed algorithm 2 that
creates a novel 67 features as proposed multi set features (combine processing). Extraction of multiset
features is supported by combining the two images (fingerprint and venous finger), as discussed before.
Accordingly, after making a comparison between the previous methods in this research paper, the highest
level of distinction for the extracted features is recorded in the fourth method and the fifth method. So, we
recommend adopting them in extracting features due to their high efficiency.
5.2. Create a new real dataset to improve the proposed system
5.2.1. The first sub dataset
In this paragraph, a new data sets resulting mentions from experimental tests. These data sets consist
of a total of 260 images depicting finger veins, and they have been positioned in a manner corresponding to
the fingers mentioned in the data labeled as Figure 3. This implies that the data sets have been carefully
curated and organized to align with the specific conditions and parameters. This information signifies a
crucial step in the research or study being conducted, indicating the availability of a well-prepared data
resource for further analysis or investigation.
5.2.2. Second sub dataset
In 260 fingerprint pictures, 26 people were chosen; each person has ten pictures of the same finger.
Each picture is from the ten pictures of the same finger taken in a different position and at different times.
were taken, in each position, one of two different images is extracted, the first is the image for the vein and
the second with a fingerprint, and that is the base of the number of 26 items each class represents a person.
Figure 4 gives an example of the first two classes in the dataset.
5.2.3. The third sub dataset
The special photos collect a fingerprint of the special needle in the same position as the fingers at
the base of the data. That gives an example of the first two classes in the dataset. Our proposed system is
applied to a large group of fingerprints and venous fingers that were randomly selected to demonstrate the

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strength of our proposed system. Our proposed algorithms were adopted in this research to feed data sets for
use in electronic document authentication and verification systems. In addition to the tests conducted on the
selected models, where all the results we obtained were satisfactory and subject to the criteria of the balance
between time and complexity, Figure 5 gives an example of computing circular projection features on canny
edge detection of Finger vein and fingerprint. It is easy and clear to explain this method through the simple
mathematical model that can be found with following equation:
∑ 𝑐𝑖𝑟𝑐𝑢𝑙𝑎𝑟_𝑝𝑟𝑜𝑗𝑒𝑐𝑡𝑖𝑜𝑛_𝑜𝑓(𝑒𝑓𝑝) + (𝑒𝑓𝑣)
360
0
where 𝑒𝑓𝑝 denotes the edge of the fingerprint image, 𝑒𝑓𝑣 denoted the edge of the finger vein image, and
circular projection computes over 360-degree projection. As samples (A, B, C, D, E, F, Z) were tested, 26
classes were tested of the items by extracting the fingerprint from the testing operations. The traditional
distance between the test samples and the items in the data set is calculated. Fifty samples of the items were
tested, but other fingerprints were in different conditions from the one that was done. Record the samples in
the collection base. Fifty samples of different varieties were tested.
The affiliation of the samples that are from the same classifications are recorded, but with different
fingerprints taken to the original items, with a high accuracy rate of most samples, reaching 100%. The lack
of affiliation with samples that are from varieties other than those registered in the system has also been
recorded. Achieves the research path which is adopted in this research by calculating the Euclidean area,
which is one of the efficient and easy methods that achieve the highest levels of balance between time and
complexity.
Class A
Finger vein 1 Finger vein 2 Finger vein 3 Finger vein 4 Finger vein 5
Finger vein 6 Finger vein 7 Finger vein 8 Finger vein 9 Finger vein 10
Figure 3. Sample of experimental results that demonstrate the image of finger vein of the first two classes as
an example class A
Class A
Fingerprint 1 Fingerprint 2 Fingerprint 3 Fingerprint 4 Fingerprint 5
Fingerprint 6 Fingerprint 7 Fingerprint 8 Fingerprint 9 Fingerprint 10
Figure 4. Samples of experimental results that demonstrate the image of fingerprints

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Figure 5. Block diagram for applying Canny edge detection of finger vein and fingerprint
5.3. Devises specification (fingerprint and finger vein scanner)
A special scanner is used to capture two different images at the same time. The first image is a
fingerprint image, while the second image is a finger vein. We used a special scanner called (FPV10R) as
shown in Figure 6. The output of this scanner is used to build real datasets, the first sub-data set for a finger
vein and the second sub-data set for a fingerprint, as we explained before. These datasets are used in the
experiment results to examine the proposed features, which in turn were used in the suggested validation
system.
Figure 6. The FPV10R scanner
6. VERIFICATION OF SECURE E-DOCUMENT USING PROPOSED SYSTEM
The combination of features derived from different patterns of fingerprint image and vein image in
this system has outperformed biometric systems that use only one biometric feature. This proves the forces of
the multiple systems that address what unilateral systems cannot handle, such as reduced accuracy in the
process of recognition, plagiarism, and non-discrimination, among other things that stand in the way of the
use of individual features of biometric systems. In this work, we have ignored and supported the techniques
and verses that depend on the details of the complex extract features related to the feature of fingerprint
extraction as well as veins, each of which has a special structure of features that may not be compatible. We
resorted to choosing the level of integration in the level of features chosen from both types to increase the
level of security and the same high efficiency. We have suggested a fast and powerful calculation of the
unique single directory using finger vein and fingerprint images. The method chosen is fast, robust, and
secure and it can be used in both document authentication and Data integrity. In our proposed biometric
system, various strategies have been envisaged, which depend on the combination of both the fingerprint
image and the image of the vein of the same finger to create a strong, effective system of high speed and
accuracy. Combining the fingerprint with the vein helped to solve the safety problem that plagued electronic
documents. These strategies have relied on efficient and less sophisticated methods.
7. MATCHING AND MATCHING SCORE
Classification in our proposed system is used using the method of closest neighbors, which depends
on measures of marital distance, as the classification of query points is done by measuring the distance from

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the training data points and this is considered a simple and effective way to classify the new points to know
the extent of their affiliation to the classes used for training. The distance scales used to find the distance
between the data set and query points differ. It can use any of these methods to calculate closest neighbors
(Euclidean distance, Cosine distance, Correlation distance, Hamming distance, Jaccard distance, and
Spearman distance). As an example, Euclidean distance has been used in this paper. Given an mx-by-n data
matrix X, which is treated as mx (1-by-n) row vectors x1, x2, ..., xmx, and an my-by-n data matrix Y, which is
treated as my (1-by-n) row vectors y1, y2, ..., ymy.
8. CONCLUSION
This idea reliably replaces the multi-solution frameworks achieved in many businesses. For
example, gives a more noteworthy diversity to precisely discovering the weaknesses of previous methods to
address them in the proposed system. The performance of the recognition system in the proposed system is
distinguished by extracting the important features of the fingerprint and linking it successfully to the veins of
the same finger to extract unique and new features that are distinctive for this finger and used in a novel way
in the proposed recognition system.
As a result of these research pursuits, one of the creative commitments of our work is the structure
of general and time-proficient protection safeguarding arrangements and conventions which might be utilized
with other biometric modalities, yet additionally, in different sorts of calculations where the security of the
information is one of the principal concerns. Furthermore, we took a shot at different regular modalities that
have diverse true applications. Accordingly, different arithmetic settings are adopted to process regular and
safe biometric accounts, as computing in this field depends on two parties. The first party represents the basic
database that contains the ideal characteristics of fingerprints and veins for authorized persons. While the
second part represents the fingerprint on the document. It is meaning that each party their entries, and they
will both work together to calculate a function and validate the document.
Our proposed system is characterized by its strength and the difficulty of its penetration, as it
depends on both fingerprints and veins at the same time, which are considered to be biometric features,
which are difficult to falsify and copy in the case of merging them, unlike other traditional methods that use
identity cards and that suffer from the easy forgery and forgery of the existing signature it must obtain a false
document. Therefore, it is necessary to think in a new way to find radical solutions to the issue of forgery and
tampering with electronic documents.
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BIOGRAPHIES OF AUTHORS
Ammar Mohammed Ali received a bachelor’s degree in computer science from
the University of Technology, Baghdad, and received an M.S. degree in computer science
(computer programming) from Harbin Engineering University, China, in 2012 and he is now a
Ph.D. student (data security) in computer science from University of Technology, Baghdad.
His research interest includes privacy, security, biometric techniques, image processing, and
pattern recognition applications. He can be contacted at ammar.m.ali@uotechnology.edu.iq.
Alaa Kadhim Farhan received the bachelor’s degree in computer science and the
M.Sc. degree in information security from the Department of Computer Science, University of
Technology, Baghdad, in 2003 and 2005, respectively, and the Ph.D. degree in information
security from the University of Technology, in 2009. He is an assistant professor at the
Department of Computer Science, University of Technology. In 2005, he joined the
Department of Computer Science, University of Technology, as an Academic Staff Member.
He has been the author of numerous technical articles, since 2008. His research interests
include cryptography, programming languages, chaos theory, and cloud computing. He can be
contacted at dralaa_cs@yahoo.com.

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