Fusion Method with Mean-discrete Algorithm in Feature level for Identical twins Identification

  • Bayan Omar Department of Information Technology, College of Science and Technology, University of Human Development, Sulaymaniyah, KRG, Iraq.

Abstract

The study on twins is an important form of study in the forensic and biometrics field as twins share similar genetic traits. A biometric is one of the common types of pattern recognition which acquires biometric data from a person. From these data, a feature is established and extracted where these features can be used to identify individual. Exiting works in biometric identification concentrate on unimodal biometric identification. The high similarity in a pair of twin’s biometric may lead to miss performance. Hence, due to their great accurateness, multimodal biometric systems have become more favored than unimodal biometric systems in identical twins identification. However, these systems are highly complex. We proposed Mean-Discrete feature based fusion algorithm for Kurdish handwriting and fingerprint for identical twins detection. Its viability and advantage over the unimodal biometric systems are highlighted. This paper employed 800 images from 50 pairs of identical twins from Kurdistan Region to carry out the experiment.

References

[1] Y. Koda, T. Higuchi and A. K. Jain. “Advances in Capturing Child Fingerprints: A High Resolution CMOS Image Sensor with SLDR Method”. 2016 International Conference of the Biometrics Special Interest Group IEEE, pp. 1-4, 2016.
[2] S. Karahan, M. Kılınc and H. K. Ekenel. “How Image Degradations Affect Deep CNN-based Face Recognition”? IEEE Conference Publications, pp. 1-5, 2016.
[3] S. Easwaramoorthy, F. Sophia and A. Prathik. “Biometric Authentication Using Finger Nails”. International Conference on Emerging Trends in Engineering, Technology and Science, IEEE Conference Publications, pp. 1-6, 2016.
[4] H, Behravan and K. Faez. “Introducing a New Multimodal Database from Twins’ Biometric Traits”. IEEE Conference Publications, IEEE, pp. 1-6, 2013.
[5] N. Srinivas, G. Aggarwal, P. J. Flynn and R. W. V. Bruegge. “Analysis of facial marks to distinguish between identical twins”. IEEE Transactions on Information Forensics and Security, vol. 7, pp. 1536-1550, 2012.
[6] C. Kauba, A. UhlWavelab, E. Piciucco, E. Maiorana and P. Campisi. “Advanced Variants of Feature Level Fusion for Finger Vein Recognition”. IEEE Conference Publications, pp. 1-7, 2016.
[7] N. Nain, B. M. Deepak, D. Kumar, M. Baswal and B. Gautham. “Optimized minutiae-based fingerprint matching”. Lecture Notes in Engineering and Computer Science, vol. 2170, pp. 682-687, 2008.
[8] W. Y. Leng and S. M. Shamsuddin. “Fingerprint identification using discretization technique”. International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 6, pp. 240-248, 2012.
[9] J. Weber-Lehmann, E. Schilling, G. Gradl, D. C. Richter, J. Wiehler and R. Burkhard. “Finding the needle in the haystack: Differentiating ‘‘identical’’ twins in paternity testing and forensics by ultra-deep next generation sequencing”. Forensic Science International: Genetics, vol. 9, pp. 42-46, 2014.
[10] H. Nejati, L. Zhang, T. Sim E. Martinez-Marroquin and G. Dong. “Wonder Ears: Identification of Identical Twins from Ear Images.” Proceedings of the 21st International Conference on Pattern Recognition, Nov. 11-15, IEEE Xplorev Press, Tsukuba, Japan, pp.1201-1204, 2012.
[11] V. Vipin, K. W. Bowyer, P. J. Flynn, D. Huang, L. Chen, M.Hansen, O. Ocegueda, S. K. Shah and I. A. Kakadiaris. “Twins 3D Face Recognition Challenge”. Proceedings of the International Joint Conference on Biometrics, Oct. 11-13, IEEE Xplore Press, Washington, DC, USA, pp. 1-7, 2011.
[12] J. R. Paone, P. J. Flynn, P. J. Philips, K. W. Bowyer, R. W. V. Bruegge, P. J. Grodher, G. W. Quinn, P. T. Pruitt and J. M. Grant. “Double trouble: Differentiating identical twins by face recognition”. IEEE Transactions on Information Forensics and Security, vol. 9, pp. 285-295, 2014.
[13] S. N. Srihari, S. H. Cha, H. Arora and S. Lee. “Individuality of handwriting”. Journal of Forensic Sciences, vol. 47, pp. 1-17, 2002.
[14] B. O. Mohammed and S. M. Shamsuddin. “Improvement in twins handwriting identification with invariants discretization”. EURASIP Journal on Advances in Signal Processing, vol. 48, pp. 1-19, 2012.
[15] A. S. Al-Waisy, R. Qahwaji, S. Ipson, S. Al-Fahdawi, T. A. M. Nagem. “A multi-biometric iris recognition system based on a deep learning approach”. Pattern Analysis and Applications, vol. 21, pp. 783-802, 2018.
[16] B. L. Priya, M. P. Rani. “Authentication of Identical Twins Using Tri Modal Matching”. World Congress on Computing and Communication Technologies, IEEE, 2017.
[17] K. M. Azah, S. M. Shamsuddin and A. Abrahamz. “Improvement of authorship invarianceness for individuality representation in writer identification”. Neural Network World, vol. 3, pp. 371-387, 2010.
[18] P. Feng P. and M. Kean. “A new set of moment invariants for handwritten numeral recognition”. IEEE International Conference of Image Processing, vol. 1, pp. 154-158, 1994.
[19] S. Yinan, L. Weijun and W. Yuechao. “United Moment Invariants for Shape Discrimination”. Proceedings of the IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, Oct. 8-13, IEEE Xplore Press, Changsha, Hunan, China, pp. 88-93, 2003.
[20] M. K. Hu. “Visual pattern recognition by moment invariants”. IEEE Transactions on Information Theory, vol. 8, pp. 179-187, 1962. [21] C. C. Chen. “Improved moment invariants for shape discrimination”. Pattern Recognition, vol. 26, pp. 683-686, 1993.
[22] B. O. Mohammed and S. M. Shamsuddin. “Twins multimodal biometric identification system with aspect united moment invariant”. Journal of Theoretical and Applied Information Technology, vol. 95, p. 2895, 2017.
[23] B. O. Mohammed and S. M. Shamsuddin. “A multimodal biometric system using global features for identical twins identification”. Journal of Computer Science, vol. 14, pp. 92-107, 2018.
[24] B. O. Mohammed and S. M. Shamsuddin. “Feature discretization for individuality representation in twins handwritten identification”. Journal of Computer Science, vol. 7, pp. 1080-1087, 2011.
Published
2020-12-27
How to Cite
OMAR, Bayan. Fusion Method with Mean-discrete Algorithm in Feature level for Identical twins Identification. UHD Journal of Science and Technology, [S.l.], v. 4, n. 2, p. 141-150, dec. 2020. ISSN 2521-4217. Available at: <https://journals.uhd.edu.iq/index.php/uhdjst/article/view/776>. Date accessed: 16 june 2021. doi: https://doi.org/10.21928/uhdjst.v4n2y2020.pp141-150.
Section
Articles