Development modeling methods of analysis and synthesis of fingerprint deformations images

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 6, December 2020, pp. 6053~6060
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i6.pp6053-6060  6053
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Development modeling methods of analysis and synthesis of
fingerprint deformations images
Haider Hassan Majeed Al Karaawi1
, Mohammed Qasim Dhahir2
, Ibrahim Ahmed Alameri3
1
University of Kerbala, Iraq
2
Department of English Language, University of Kufa, Iraq
3
Jaber ibn Hayyan Medical University, Iraq
3
Faculty of Economics and Administration, University of Pardubice, Czech Republic
1,2,3
Ministry of Higher Education and Scientific Research, Iraq
Article Info ABSTRACT
Article history:
Received Mar 8, 2020
Revised May 14, 2020
Accepted May 26, 2020
The current study is to develop modeling methods, analysis and synthesis of
fingerprints deformations images and their application in problems of
automatic fingerprint identification. In the introduction justified urgency of
the problem, is given a brief description of thematic publications. In this study
will review of modern technologies of biometric technologies and methods of
biometric identification, the review of fingerprint identification systems,
investigate for distorting factors. The influence of deformations is singled out,
the causes of deformation of fingerprints are analyzed. The review of modern
ways of the account and modeling of deformations in problems of automatic
fingerprint identification is given. The scientific novelty of the work is
the development of information technologies for the analysis and synthesis of
deformations of fingerprint images. The practical value of the work in
the application of the developed methods, algorithms and information
technologies in fingerprints identification systems. In addition, it has been
found that our paper "devoted to research methods and synthesis of
the fingerprint deformations" is a more appropriate choice than other papers.
Keywords:
Correlation
Dactyloscopy
Deformations
Heterogeneity
Minimum spanning tree (MST)
Orthonormality
Papillary
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Mowafak K. Mohsen,
Ministry of Higher Education and Scientific Research,
University of Kerbala,
Kerbala, Iraq.
Email: mowafak.k.m@gmail.com
1. INTRODUCTION
Currently, biometric (using the physical and behavioral characteristics of a person) [1, 2] personality
identification technologies and systems based on them are widely used in various fields [3, 4]: from access to
premises to electronic commerce and government systems for various purposes. The most common biometric
identification method is fingerprint identification [5-7]. Despite numerous studies in the field of fingerprinting
conducted by such scientists, the accuracy of fingerprint identification has not reached its potential. The main
reason is the complexity of modeling, accounting and compensation of numerous distorting factors, among
which are noise, small areas of intersection of fingerprints presented for comparison, and elastic deformations.
The most important applications of fingerprint deformation models are the synthesis of artificial finger
applications to simulate technological tests in civil identification systems, and a priori elimination of
deformation by calculating the “non-deformed” state of a fingerprint for a variety of applications at
the registration stage. Deformations are one of the strongest and least studied factors. In this regard, the topics
related to the study of deformation of fingerprints is relevant. For achievement of the goals need a solution of
the tasks, some development of mathematical model for processing of fingerprint deformation have been
conducted, i.e: development of methods for analyzing of fingerprint deformities images [8], statistical analysis

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 6, December 2020 : 6053 - 6060
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of character and the structure of deformations; development of methods for the synthesis of fingerprint
deformities images [9], development of the modeling algorithm for the fingerprint deformation [10],
and development of a priori registration algorithm for the fingerprint deformation [11]. In this paper, modern
technologies of biometric technologies, methods of biometric identification and fingerprint identification
systems will be reviewed; distorting factors will be investigated; and deformation of fingerprints will be
analyzed.
2. RESEARCH METHOD
This work uses modern methods of image processing and image recognition [12, 13], probability
theory and mathematical statistics [14], theory of elasticity, methods of programming and computer
simulation [15]. The deformation of the fingerprint image is determined by a displacement map, which can be
interpreted as displaying one image to another image:
ƒ=Im1→ Im2 (1)
where Im1, Im2 - images of fingerprints.
Next, we use the representation of the map (1) in the form of a displacement map u. The action of
the mapping ƒu , associated with displacements u, can be represented by the following expression:
𝐼𝑚1( 𝑥, 𝑦) = 𝐹𝑢
−1
(𝐼𝑚2( 𝑥, 𝑦)) =
=
1
𝑑𝑒𝑡 𝐽 𝑢 (𝑥, 𝑦)
𝐼𝑚2( 𝑥 + 𝑢 𝑥(𝑥, 𝑦), 𝑦 + 𝑢 𝑦(𝑥, 𝑦)),
𝐽 𝑢(𝑥, 𝑦) =
(
1 +
𝜕𝑢 𝑥
𝜕𝑥
𝜕𝑢 𝑥
𝜕𝑦
𝜕𝑢 𝑦
𝜕𝑥
1 +
𝜕𝑢 𝑦
𝜕𝑦 )
(2)
In dynamics as shown in Figure 1, we can assume that u it depends on time. Suppose that
a fingerprint is an elastic object. Then its dynamics is described by the following system of equations of
the theory of elasticity:
𝜕𝜎𝑥𝑦
𝜕𝑦
+
𝜕𝜎𝑥𝑥
𝜕𝑥
+ 𝑓𝑥 = 𝜌
𝜕2
𝑢 𝑥
𝜕𝑡2
;
𝜕𝜎𝑥𝑦
𝜕𝑥
+
𝜕𝜎 𝑦𝑦
𝜕𝑦
+ 𝑓𝑦 = 𝜌
𝜕2
𝑢 𝑦
𝜕𝑡2
(3)
𝜀 𝑥𝑥 =
𝜕𝑢 𝑥
𝜕𝑥
; 𝜀 𝑦𝑦 =
𝜕𝑢 𝑦
𝜕𝑦
; 𝜀 𝑥𝑦 = 𝜀 𝑦𝑥 =
1
2
(
𝜕𝑢 𝑥
𝜕𝑦
+
𝜕𝑢 𝑦
𝜕𝑥
) (4)
where ux and uy – components of the displacement map along the x and y axes respectively, - acting external
forces, ɛ - tensor of 𝑓 = (𝑓𝑥, 𝑓𝑦) tension, σ - pressure tensor, ρ the density. Components of tensors ɛ and σ are
related by a linear relationship:
(
𝜕𝑥𝑥
𝜕 𝑦𝑦
𝜕𝑥𝑦
) = 𝑪(𝑥, 𝑦) (
𝜀 𝑥𝑥
𝜀 𝑦𝑦
𝜀 𝑥𝑦
) (5)
Solutions of equations are determined by the values of the elasticity matrix𝑪(𝑥, 𝑦). The matrix is
unstable, since the surface of the finger is not uniform in mechanical properties. Heterogeneity is associated
with various mechanisms of deformation under the action of forces along and across the direction of
the papillary pattern [16], as shown in Figure 2. In the coordinates associated with the local direction of
the papillary pattern, matrix 𝑪€
has four degrees of freedom and is equal to 1:
𝑪€
(𝑧, 𝑤) = (
𝐸𝑧
−1
−𝑣𝑧𝑤 𝐸 𝑤
−1
0
−𝑣 𝑤𝑧 𝐸𝑧
−1
𝐸 𝑤
−1
0
0 0 𝐺
) ; 𝑣𝑧𝑤 𝐸 𝑤
−1
= −𝑣 𝑤𝑧/𝐸𝑧
−1
(6)

Int J Elec & Comp Eng ISSN: 2088-8708 
Development modeling methods of analysis and synthesis of ... (Haider Hassan Majeed Al Karaawi)
6055
where 𝐸𝑧, 𝐸 𝑤 - modules along and across the direction field, 𝑣𝑧𝑤and 𝑣 𝑤𝑧 - Poisson's ratio, G - Shear modulus.
When changing to the original global coordinates (6).
Figure 1. The process of deformation of fingerprints (screencast)
Figure 2. Direction of the flow of papillary lines
3. RESULTS AND DISCUSSION
In fingerprint identification the main interest is the moment of image capture [16, 17]. Usually this
happens, when the image is still, the acting forces have already balanced the tension, cause displacement. Such
a condition can be written as the following equation
𝜕𝜎𝑥𝑦
𝜕 𝑦
+
𝜕𝜎𝑥𝑥
𝜕𝑥
+ 𝑓𝑥 = 0;
𝜕𝜎𝑥𝑦
𝜕𝑥
+
𝜕𝜎 𝑦𝑦
𝜕 𝑦
+ 𝑓𝑦 = 0; (7)
The system of (7) and (4) determines the deformation of the fingerprint. The following difficulties of
its direct solution are noted. First, there is no initial state of the print, from which the deformation begins.
First, there is no initial state of the print, from which the deformation begins. Deformation is a consequence of
the process of two-dimensional scanning of a three-dimensional object see Figure 3. Secondly, the active forces
are unknown. There is no engineering way to measure them. From the theory of elasticity, it is known that
the solution of (4) and (7) can be sought as a minimum of the following energy function
𝐸 = − ∬(𝑢 𝑥 𝑓𝑥 + 𝑢 𝑦 𝑓𝑦)𝑑𝑥𝑑𝑦 +
1
2
∬(𝜀 𝑥𝑥 𝜎𝑥𝑥 + 𝜀 𝑦𝑦 𝜎 𝑦𝑦 + 𝜀 𝑥𝑦 𝜎𝑥𝑦)𝑑𝑥𝑑𝑦
𝑆𝑆
(8)
In connection with the above-mentioned problem of the inability to estimate the effective forces,
the negative term in (8) also cannot be directly evaluated. For its elimination, a method of approximate
deformation calculation is proposed. For two random images of one fingerprint, will transforming one into
another by deformation, you can find corresponding to each other control points of the image as shown in
Figure 4, which gives information on the general direction of the acting forces.

 ISSN: 2088-8708
6056
Figure 3. The appearance of deformation of fingerprints
Figure 4. Matching fingerprint checkpoints
The sets of pairs of points corresponding to each other are denoted by {𝒑𝑖}𝑖=1
𝑚
and {𝒒𝑖}𝑖=1
𝑚
respectively.
As a measure of proximity of two sets of control points, the mean square error
𝐿2
(𝒖) =
1
𝑚
∑ ∥ 𝒑𝑖 + 𝒖(𝒑𝑖) − 𝒒𝑖 ∥2𝑚
𝑖=1 (9)
The functional (9) is qualitatively correlated with the work of the acting forces of the energy functional (8).
If a control point shift occurs, it is assumed that external forces act in the direction of displacement.
In addition, it is also suggested that the directions of the fluxes of the papillary lines at the control points match
with the following functional:
𝐿 𝑛
2
(𝒖) =
1
𝑚
∑ ∥ 𝒏(𝒑𝑖 + 𝒖(𝒑𝑖)) − 𝒏(𝒒𝑖) ∥2
𝑚
𝑖=1
(10)
where n - functional of taking the normal to the local flow of papillary lines. With allowance for (9) and (10),
it is suggested to seek the approximate solution (8) as a minimum of the following functional, consisting of
the internal energy of deformation and regularizing the corrections to the discrepancy:
𝐷(𝒖) = 𝐸 𝑑(𝒖) + 𝑎1 𝐿2
(𝒖) + 𝑎2 𝐿 𝑛
2
(𝒖) (11)
where a1 , a2 - weight coefficients, internal strain energy Ed is calculated by the formula.
𝐸 𝑑 =
1
2
∬ (𝜀 𝑥𝑥 𝜎𝑥𝑥 + 𝜀 𝑦𝑦 𝜎 𝑦𝑦 + 𝜀 𝑥𝑦 𝜎𝑥𝑦)𝑑𝑥𝑑𝑦𝑆
(12)
The minimum of the functional (11) is sought numerically by the finite element method.
The displacement map is specified by values in a rectangular grid. For points within a grid element, the values
u are interpolated by bilinear splines. With such interpolation (11) is a quadratic functional of the values at
the lattice sites. Thus, the minimum is sought from the condition that all partial derivatives vanish.
This condition is a linear equation for the values u in the lattice sites. The developed algorithm of the solution
quickly converges. On average, the calculation of the relative deformation takes less than 0.1 s. Examples of
the results of deformation calculations are shown in Figure 5. From the point of view of the research tasks,
the optimal result is to find a deformation that translates one image of the print into another image of the same

6057
print. Therefore, as one of the estimates of the accuracy of deformation calculation, direct overlap is used
Imav images:
𝑰𝒎 𝑎𝑣 =
1
2
(𝑰𝒎2 + 𝑓𝑢(𝑰𝒎1)) (13)
Examples of superposition with allowance for the deformation optimal for (11) in comparison with
the optimal motion are shown in Figure 5. The contrast part of the results of superposition indicates
the resonance of the papillary lines. The gray part corresponds to inaccurate guidance. It can be seen from
the figure that the proposed model allows the image of fingerprints to be accurately converted into each other
with high accuracy. There are no edge effects characteristic of empirical deformation models based on a thin
metal plate2 or the Cappelli et al., 3 model. Also, the advantages of the model include the possibility of
calculating the relative strain for arbitrary dactyloscopy images for which the corresponding reference points
can be found, and for presenting various fingerprints [18].
(a) (b) (c) (d)
Figure 5. Example of calculation of the deformation (a), (b) two images of the print,
(c) direct overlap when fitting by hard motion, (d) direct overlap after fitting the deformation
The conducted statistical analysis also confirms that the proposed elastic model significantly improves
the accuracy of image guidance in comparison with known models. As an objective criterion for the accuracy
of modeling the process of deformation of fingerprints, the direct imposition (correlation) coefficient for
binarized fingerprint images is used [19]. DET curves of the coefficient of direct overlap for the base
FVC2002DB1 (moderate deformation) and FVC2004DB1 (significant deformations) are shown in Figure 6.
As can be seen from the figure, the proposed elastic model significantly improves the imposition of images in
comparison with the rigid motion and deformation modeling by the TPS method.

 ISSN: 2088-8708
6058
── optimal movement
─◊─ accounting for deformations (model TPS)
─□─ accounting for deformations (elastic model)
Figure 6. DET of the direct overlap factor (FVC2002DB1) and (FVC2004DB1)
In this study devoted to methods of analysis and synthesis of deformations of fingerprints.
To determine the deformation structure, an analysis was made of the numerical solutions of (9) on publicly
available fingerprint databases. Since (8) is linear, its solutions (deformations) can be added and multiplied by
a number. For deformations, operations of the scalar product and projection onto each other are also defined.
Therefore, it is proposed to study the structure of deformations by linear expansion in terms of an "orthonormal"
set of basis deformations [20].
Basic deformations can be calculated both from qualitative considerations, and through formal
statistical analysis. Based on the expansion (13), an approximate linear model is developed. Coefficients ci are
calculated by minimizing the discrepancy (9) and (10). As basic deformations, it is suggested to use
the "principal" deformations obtained by the main component method on the reference fingerprint
array [21, 22]. To calculate the main components, three FVC2002 databases were used, containing images
from optical and capacitive fingerprint scanners [23, 24]. Each database contains 8 samples for 100 people, i.e.
for the analysis of deformities, there are three sets of 2800 natural deformations of fingerprints in total [25, 26].
In Figure 7 shows the first four basic deformations obtained by the principal component method.
They allow a physical interpretation (two types of rotation and two shifts) see Figure 8. Such deformations
correspond to the subjective concept of the main ways of applying a finger to the scanner. In addition to
the qualitative analysis of deformations, a number of formal criteria for the accuracy of the deformation
approximation were considered, which showed a rather high accuracy of the linear deformation model.
The first 8-10 main components provide a sufficiently accurate approximation of the observed strains
for any of the above accuracy criteria. At the same time, the main components obtained are practically
independent of the fingerprint array chosen for analysis [27, 28]. Differences in the nature of the deformations
are in the various observed distributions of the coefficients ci of the expansion (12). From the data obtained,
in particular, the following conclusions were: The statistical properties of natural deformations are practically
independent of the type of scanner model (cases a and b). Synthetic deformations obtained by methods known
in the past differ in their characteristics (c). Also, the quantitative difference between the deformations in
the rolling of a print and the deformation in electronic scanning (live scan) is established. Then deals with
the application of the developed methods of analysis and synthesis of deformations to real problems of
fingerprint identification: Synthesis of artificial deformations of fingerprints and synthesis of "not-deformed"
fingerprint to increase the accuracy of fingerprint identification [29]. The creation of a "not-deformed" averaged
image from several fingerprints is a special case of the synthesis of artificial deformation. Let there be k + 1
images: Im0 , Im1 , …. Imk. Relative deformations Imi in Im0 are denoted by ui. Two approaches to
the synthesis of mean strain are considered. The first is to formally calculate the average displacement map uav:
The average image is determined as Imav=uav(Im0). The second approach is to calculate the state of
the print, in which the intrinsic energy of the deformations to the images from the collection was minimal:
The average for this condition is defined as Immin=umin(Im0). The resulting "not-deformed" image is used to
increase the accuracy of identification. When registering on the basis of several fingerprints, the average state
is calculated shown Figure 9. The solution (12) depends only on the mutual arrangement of control points in
various applications of the finger. Therefore, the not-deformed state can be calculated not only for the image
of the fingerprint, but also for the fingerprint template. Thus, the synthesis of the "not-deformed" state can be
introduced into functioning automatic dactyloscopic verification and identification systems [30, 31].

6059
(a) (b) (c) (d)
Figure 7. Map of the displacements of the main deformations obtained on different fingerprint arrays,
(a), (b) deformation rotation, (c), (d) deformation shifts
(a) (b) (c) (d)
Figure 8. Schematic representation of the main deformations, "H" - relatively fixed area, the arrow indicates
the main directions of displacement, (a), (b) deformation rotation, (c), (d) deformation shifts
Figure 9. Averaged deformation fingerprint image (Immin)
4. CONCLUSION
A model of elastic fingerprint deformations based on the picture of the print as an anisotropic elastic
material is suggested taking into consideration the heterogeneity of fingerprint mechanical properties in
the sense of the local projection of the deformation direction in the local direction of the papillary line flows.
An approximate methods and algorithms have been developed for fingerprint deformation calculations based
on solution of elastic deformation equations that help the mechanical characteristics of fingerprints. Methods
are built for the study and synthesis of fingerprint image deformations based on the expansion of deformations
by major types of applied force (deformation movements and rotations). A feature system has been developed
that characterizes the deformation structure. The composition of the fingerprint deformations was statistically
and qualitatively analyzed for the various fingerprint databases. An information technology has been developed
to synthesize artificial deformations with statistical characteristics of observed natural deformations.
An information technology has been developed in order to synthesize a "non-deformed" fingerprint prototype
for fingerprint recognition and testing systems.

 ISSN: 2088-8708
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