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Individual biometrics pattern based artificial image analysis techniques

  • * Corresponding author: Israa Mohammed Khudher

    * Corresponding author: Israa Mohammed Khudher 
Abstract Full Text(HTML) Figure(4) / Table(5) Related Papers Cited by
  • 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.

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


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  • Figure 1.  Bee Colony procedure

    Figure 2.  A sample from the visual database

    Figure 3.  A sample from the visual database

    Figure 4.  The system outcome for acceptance message

    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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  • [1] M. M. A. AbuqadumahM. A. M. AliA. A. Almisreb and B. Durakovic, Transfer learning for human identification based on footprint: a comparative study, Periodicals of Engineering and Natural Science, 7 (2019), 1300-1307. 
    [2] V. D. Ambeth Kumar and M. Ramakrishnan, Footprint recognition using modified sequential haar energy transform (MSHET), IJCSI International Journal of Computer Science, 7 (2010).
    [3] V. Bachu and J. Anuradha, A review of feature selection and its methods, Cybernetics and Information Technologies, 19 (2019), 1-26.  doi: 10.2478/cait-2019-0001.
    [4] F. Baji and M. Mocanu, Chain code approach for shape-based image retrieval, Indian Journal of Science and Technology, 11 (2018), 1-17. 
    [5] J. C. BansalH. Sharma and S. S. Jadon, Artificial bee colony algorithm: a survey, International Journal Advanced Intelligence Paradigms, 5 (2013), 123-159. 
    [6] M. A. Bin-Basir and F. Binti-Ahmad, Comparison on swarm algorithms for feature selections/reductions, International Journal of Scientific and Engineering Research, 5 (2014), 479-486. 
    [7] M. Boelkins, D. Austen and S. Schlicker, Active Calculus 2.0, 2$^{nd}$ edition, Grand Valley State University Libraries Publisher, USA, 2017.
    [8] C. Chidambaram and H. S. Lopes, An improved artificial bee colony algorithm for the object recognition problem in complex digital images using template matching, International Journal of Natural Computing Research, 1 (2010), 54-70. 
    [9] E. Cuevas, F. Sencin-Echauri, D. Zaldivar and M. Prez, Image Segmentation Using Artificial Bee Colony Optimization, 1$^{st}$ edition, Springer-Verlag, Berlin Heidelberg, 2013.
    [10] T. DavidoviD. Teodorovi and M. Selmic, Bee colony optimization part I: The algorithm overview, Yugoslav Journal of Operations Research, 25 (2015), 33-56.  doi: 10.2298/YJOR131011017D.
    [11] M. U. Farooq, Q. Salman, M. Arshad, I. Khan, R. Akhtar and S. Kim, An artificial bee colony algorithm based on a multi-objective framework for supplier integration, Applied Science, 9 (2019).
    [12] L. GhoualmiA. Draa and S. Chikhi, An ear biometric system based on artificial bee and the scale invariant feature transform, Expert Systems with Applications, 57 (2016), 49-61. 
    [13] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2$^{nd}$ edition, Prentice-Hall, New Jersey, USA, 2002.
    [14] V. Govindaraju, Z. Shi and J. Schneider, Feature Extraction Using a Chain Coded Contour Representation of Fingerprint Images, Audio- and Video-Based Biometric Person Authentication, 2$^{nd}$ edition, Springer-Verlag Berlin Heidelberg, 2003.
    [15] Y. I. Ibrahim and I. M. Alhamdani, A hybrid technique for human footprint recognition, International Journal of Electrical and Computer Engineering (IJECE), 9 (2019), 4060-4068. 
    [16] S. O. Kamel and M. Nour, Feature selection methods for predicting the popularity of online news: comparative study, and a proposed method, Journal of Theoretical and Applied Information Technology, 96 (2018), 6969-6980. 
    [17] D. KarabogaB. GorkemliC. Ozturk and N. Karaboga, A comprehensive survey: artificial bee colony (abc) algorithm and applications, Artificial Intelligence Review, 42 (2014), 21-57. 
    [18] R. Khokher and R. Chandra Sin, Footprint-based personal recognition using dactyloscopy technique, Industrial Mathematics and Complex Systems, 1 (2017), 207-219. 
    [19] I. M. Khudher and Y. I. Ibrahim, Swarm intelligent hybridization biometric, Indonesian Journal of Electrical Engineering and Computer Science, 18 (2020), 385-395. 
    [20] S. KothuriP. Annapurna and S. Lukka, Digit recognition using freeman chain code, International Journal of Application or Innovation in Engineering and Management (IJAIEM), 2 (2013), 362-365. 
    [21] K. S. KumarK. Ventakalakshmi and K. Karthikeyan, Threshold based lung image segmentation with robust artificial bee colony algorithm optimization technique, Asian Journal of Information Technology, 15 (2016), 4426-4430. 
    [22] A. KumarD. Kumar and S. K. Jarial, A review on artificial bee colony algorithms and their applications to data clustering, Cybernetics and Information Technologies, 17 (2017), 3-28.  doi: 10.1515/cait-2017-0027.
    [23] S. B. Mirza and A. A. Waoo, A comprehensive study in wireless sensor network (wsn) using artificial bee colony (abc) algorithms, Journal, 6 (2019), 873-879. 
    [24] K. K. Nagwanshi, Cyber-forensic review of human footprint and gait for personal identification, IAENG International Journal of Computer Science, 46 (2019), 1-17. 
    [25] K. K. Nagwanshi and S. Dubey, Biometric authentication using human footprint, International Journal of Applied Information Systems, 3 (2012), 1-6. 
    [26] K. K. Nagwanshi and S. Dubey, Mathematical modeling of footprint based biometric recognition, International of mathematical trends and technology (IJMIT), 54 (2018), 49-61. 
    [27] V. R. Naramala and B Raveendrababu, Combined histogram chain code feature extraction method to recognize handwritten digits with the probabilistic neural network, International Journal of Applied Engineering Research, 9 (2014), 4585-4589. 
    [28] S. Nebti and A. Boukerram, Handwritten digits recognition based on swarm optimization methods, Networked Digital Technologies. Communications in Computer and Information Science, 87 (2010), 45-54. 
    [29] M. OliveiraD. PinheiroM. MacedoC. Bastos-Filho and R. Menezes, Uncovering the social interaction in swarm intelligence with network science, Applied Network Science, 1 (2018), 1-23. 
    [30] M. W. Powers, Evaluation: from precision, recall, and f-factor to ROC, informedness, markedness and correlation, Journal of Machine Learning Technologies, 2 (2007), 37-63. 
    [31] K. R. Raji and W. Xiaopeng, Study of biometric identification method based on naked footprint, International Journal of Science and Engineering, 5 (2013), 29-35. 
    [32] W. RongW. Hong and N. Yang, The research on footprint recognition method based on wavelet and fuzzy neural network, Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems, 3 (2009), 428-432. 
    [33] N. RusdiZ. R. YahyaN. Roslan and W. Z. Azman, Reconstruction of medical images using artificial bee colony algorithm, Mathematical Problems in Engineering, 2 (2018), 1-7. 
    [34] A. M. SalemA. A. Sewisy and U. A. Elyan, A vertex chain code approach for image recognition, International Journal on Graphics, Vision and Image Processing, 5 (2005), 1-8. 
    [35] M. Shokouhifar and G. S. Abkenar, An artificial bee colony optimization for mri fuzzy segmentation of brain tissue, International Conference on Management and Artificial Intelligence, 6 (2011).
    [36] E. B. Tirkolaee, A. Gol and G. W. Weber, Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm for just-in-time energy-aware flow shop scheduling problem with outsourcing option, IEEE Transactions on Fuzzy Systems, 1 (2020).
    [37] E. B. TirkolaeeM. AlinaghianA. A. RahmanimM. B. Sasia and A. K. Sangaiahc, An improved ant colony optimization for the multi-trip capacitated arc routing problem, Computers and Electrical Engineering Journal, 77 (2019), 457-470. 
    [38] E. B. TirkolaeeI. MahdaviM. M. Esfahani and G. W. Weber, A hybrid augmented ant colony optimization for the multi-trip capacitated arc routing problem under fuzzy demands for urban solid waste management, Waste Management and Research: The Journal for a Sustainable Circular Economy, 38 (2020), 156-172. 
    [39] U. UludagS. PankantiS. Prabhakar and A. Jain, Biometric Cryptosystems: issues and challenges, Proceedings of the IEEE, 92 (2004), 948-960. 
    [40] L. YangX. SunL. PengJ. Shao and T. Chi, An improved artificial bee colony algorithm for optimal land-use allocation, International Journal of Geographical Information Science, 26 (2015), 1470-1489. 
    [41] T. ZhangB. DingX. Zhao and Q. Yue, A fast feature selection algorithm based on swarm intelligence in acoustic defect detection, IEEE Access, 6 (2018), 28848-28858. 
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