Individual biometrics pattern based artificial image analysis techniques

Israa Mohammed Khudher; Yahya Ismail Ibrahim; Suhaib Abduljabbar Altamir

doi:10.3934/naco.2020056

Article Contents

2021, Volume 11, Issue 4: 567-578. Doi: 10.3934/naco.2020056

This issue Previous Article Global and regional constrained controllability for distributed parabolic linear systems: RHUM approach Next Article A novel hybrid AGWO-PSO algorithm in mitigation of power network oscillations with STATCOM

Individual biometrics pattern based artificial image analysis techniques

Department of Computer Sciences, College of Education for Pure Sciences, University of Mosul, Mosul, Iraq

^* Corresponding author: Israa Mohammed Khudher
^* Corresponding author: Israa Mohammed Khudher

Received: January 2020

Revised: October 2020

Early access: March 2021

Published: December 2021

Abstract / Introduction Full Text(HTML) Figure(4) / Table(5) Related Papers Cited by

Abstract

Biometric characteristics have been used since antiquated decades, particularly in the detection of crimes and investigations. The rapid development in image processing made great progress in biometric features recognition that is used in all life directions, especially when these features recognition is constructed as a computer system. The target of this research is to set up a left foot biometric system by hybridization between image processing and artificial bee colony (ABC) for feature choice that is addressed within artificial image processing. The algorithm is new because of the rare availability of hybridization algorithms in the literature of footprint recognition with the artificial bee colony assessment. The suggested system is tested on a live-captured ninety colored footprint images that composed the visual database. Then the constructed database was classified into nine clusters and normalized to be used at the advanced stages. Features database is constructed from the visual database off-line. The system starts with a comparison operation between the foot-tip image features extracted on-line and the visual database features. The outcome from this process is either a reject or an acceptance message. The results of the proposed work reflect the accuracy and integrity of the output. That is affected by the perfect choice of features as well as the use of artificial bee colony and data clustering which decreased the complexity and later raised the recognition rate to 100%. Our outcomes show the precision of our proposed procedures over others' methods in the field of biometric acknowledgment.

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Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

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Figure 1. Bee Colony procedure

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Figure 2. A sample from the visual database

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Figure 3. A sample from the visual database

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Figure 4. The system outcome for acceptance message

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Table 1. Summary of biometric features related papers

Biometric Type	Technique	Performance	Version
Foot-tip	Morphology, statistical	83.38 - 89.52	2019 [13]
Foot-tip	Modified Sequential Haar Energy Transform (MSHET)	92.37	2019 [19]
Foot-tip	texture and shape	99	2016 [26]
Foot-tip	Modified Haar Energy (MHE)	93	2016 [36]
Foot-tip	(ABC) Algorithm for finding the curve fitting	97.15	2010 [14]
Lung image	(ABC) to segment clinical	99.2	2016 [12]
Ear print	Extracting the most discriminant key-points	99.6	2010 [17]
Foot-tip	Fuzzy neural network	90-92.80	2010 [36]
Foot-tip	Suggested work Statistical Chain code-based (ABC)	100	2020

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Table 2. Features description

Moment title	Equation	Parameter
Mean	$ M_i = \frac{1}{N} \sum_{j=1}^{N} fit_i $ (5)	Where $ f_ij $ is the value of the feature and N Denotes features frequency [27].
Description	This factor is directly proportional to brightness.	The big value the more brightness and vice versa [27].
Moment title	Equation	Parameter
Standard Deviation (STD)	$ \varsigma = (\frac{1}{N} \sum_{j=1}^{n} (f_i - M_i)^2)^{\frac{1}{2}} $ (6)	Where Mi represents the average of the image, $ f_ij $ denotes the value of the feature and N reflects the observation size [27].
Description	This factor is inversely proportional to image contrast. The big value the small contrast and vice versa [27].
Center-Angle	$ \Theta = \| atan^2 (y, x) \pi / 180 $ (7)	y, x are convoluted images with specific masks[27].
Description	This factor describes the direction of the chain code.
Mean, Std of Histogram of the Chain Code	=	=
Description	These factors describe the intensity and contrast of the Chain Code [22]

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Table 3. The experimental results

Query name	Bestsolution	Image number	Cluster	Time in sec
Qry1	0	4	1	0.2888
Qry2	0.0740	13	2	0.1215
Qyr3	0.2932	28	3	0.1318
Qry4	0	31	4	0.1261
Qry5	0.0765	42	5	0.1196
Qry6	1.6607	52	6	0.1215
Qry7	0	61	7	0.1222
Qry8	0	71	8	0.1204
Qry9	0	82	9	0.1207
Qry10	0	83	9	0.1210

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Table 4. Enhancement metrics

Metric	Value
Accurecy	100%
Confidence	100%

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Table 5. Enhancement analysis of related work

Moment title	Equation	Parameter
Technique	Author	Accuracy Rate %
(ABC) Algorithm for finding the curve fitting and Euclidean	K. K. Nagwanshi and S. Dubey [14]	97.15
distance for foot-tip	K. K. Nagwanshi and S. Dubey [15]	85
Fuzzy Neural Networks + Geometrical Characteristics	W. Rong et al. [40]	90-92.80
(ABC) for Area Allocation	L. Yang et al. [18]	67.4-67.7
(ABC) for object recognition	C. Chidambaram and H. S. Lopes [3]	88-99
(ABC)+Fuzzy C Mean	M. Shokouhifar and G. S. Abkenar [24]	98.38
(ABC) for Handwritten recognition	S. Nebti and A. Boukerram [29]	99.82
The suggested paper (ABC)+Chain code+ statistical features	Israa M. Kh., et.al.	100

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