Open Access   Article

Biometric Recognition System: A Review

Manpreet Kaur1 , Sawtantar Singh Khurmi2

1 Dept. of Computer Science and Applications, DBU, Mandi Gobindgarh, India.
2 Dept. of Computer Science and Engineering, DBU, Mandi Gobindgarh, India.

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Section:Review Paper, Product Type: Journal Paper
Volume-5 , Issue-9 , Page no. 40-45, Sep-2017


Online published on Sep 30, 2017

Copyright © Manpreet Kaur, Sawtantar Singh Khurmi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Manpreet Kaur, Sawtantar Singh Khurmi, “Biometric Recognition System: A Review”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.40-45, 2017.

MLA Style Citation: Manpreet Kaur, Sawtantar Singh Khurmi "Biometric Recognition System: A Review." International Journal of Computer Sciences and Engineering 5.9 (2017): 40-45.

APA Style Citation: Manpreet Kaur, Sawtantar Singh Khurmi, (2017). Biometric Recognition System: A Review. International Journal of Computer Sciences and Engineering, 5(9), 40-45.

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Biometric system is used for identification of an individual on the basis of their physical and behavioral features. As the research in the information technology is increasing day by day, so, the security of information becomes a great issue. Therefore, to deal with security, authentication access control plays an important role and this is the first step to ensure security. This paper describes the study of widely used biometric technologies. The principle by the biometric system work is being defined with the stages by which biometric system works. In biometrics, according to some characteristics, we need to identify human physiological parameters. The comparison of biometric traits on the basis of feature description is given with their characteristics on the basis of uniqueness, university, measurability, acceptability, circumvention and premenance. Work done by number of authors in biometric system is given in the form of comparison with the techniques and outcomes. A biometric system requires a reliable personal identification scheme to confirm or determine the needs of their individual identity services. The aim of this technique is to ensure that only legitimate users can access these services, and are not accessible to others. The notable features of biometric can be confirmed or established a personal identity.

Key-Words / Index Term

Biometric, fingerprint hand, iris, face, DNA, keystroke, signature, Voice


[1] M. O. Oloyede and G. P. Hancke, “Unimodal and Multimodal Biometric Sensing Systems: A Review,” IEEE Access, Vol. 4, No. , pp. 7532-7555, 2016.
[2] S. B. Dabhade, N. S. Bansod, Y. S. Rode, M. M. Kazi and K. V. Kale, “Hyper spectral face image based biometric recognition,” International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, pp. 559-561,2016.
[3] M. Abdelazez, M. Hozayn, G. S. K. Hanna and A. D. C. Chan, “Gating of false identifications in electrocardiogram based biometric system,” IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rochester, MN, USA, pp. 338-343,2017.
[4] A. Mansour, M. Sadik, E. Sabir and M. Jebbar, “AMBAS: An Autonomous Multimodal Biometric Authentication System,” 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, Spain, pp. 2098-2104,2017.
[5] S. Bhilare, V. Kanhangad and N. Chaudhari, “Histogram of oriented gradients based presentation attack detection in dorsal hand-vein biometric system,” Fifteenth IAPR International Conference on Machine Vision Applications (MVA), Nagoya, Japan, pp. 39-42,2017.
[6] Subban, Ravi, and Dattatreya P. Mankame, “A study of biometric approach using fingerprint recognition,” Lecture Notes on Software Engineering Vol.1, pp.209-215,2013.
[7] R. Priya, V. Tamilselvi and G. P. Rameshkumar, “A novel algorithm for secure Internet Banking with finger print recognition,” International Conference on Embedded Systems (ICES), Coimbatore, pp. 104-109,2014.
[8] B. Saropourian, “A new approach of finger-print recognition based on neural network,” 2nd IEEE International Conference on Computer Science and Information Technology, Beijing, pp. 158-161,2009.
[9] S. Samoil and S. N. Yanushkevich, “Multispectral hand recognition using the Kinect v2 sensor,” IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, pp. 4258-4264,2016.
[10] Lihong Wan, Na Liu, Hong Huo and Tao Fang, “Face Recognition with Convolutional Neural Networks and subspace learning,” 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, China, pp. 228-233,2017.
[11] N. Liu, J. Liu, Z. Sun and T. Tan, “A Code-Level Approach to Heterogeneous Iris Recognition,” IEEE Transactions on Information Forensics and Security, Vol. 12, No. 10, pp. 2373-2386, Oct. 2017.
[12] P. Michaels, S. Ciampi, C. Y. Yean and J. J. Gooding, “Target DNA recognition using electrochemical impedance spectroscopy,” International Conference on Nanoscience and Nanotechnology, Sydney, NSW, pp. 282-284,2010.
[13] G. K. Berdibaeva, O. N. Bodin, V. V. Kozlov, D. I. Nefed`ev, K. A. Ozhikenov and Y. A. Pizhonkov, “Pre-processing voice signals for voice recognition systems,” 18th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM), Erlagol (Altai Republic), Russia, pp. 242-245,2017.
[14] P. Chauhan, S. Chandra and S. Maheshkar, “Static digital signature recognition and verification using neural networks,” 1st India International Conference on Information Processing (IICIP), Delhi, India, pp. 1-6,2016.
[15] N. Çalik, O. C. Kurban, A. R. Yilmaz, L. D. Ata and T. Yildirim, “Signature recognition application based on deep learning,” 25th Signal Processing and Communications Applications Conference (SIU), Antalya, pp. 1-4,2017.
[16] A. Morales, M. Falanga, J. Fierrez, C. Sansone and J. Ortega-Garcia, “Keystroke dynamics recognition based on personal data: A comparative experimental evaluation implementing reproducible research,” IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), Arlington, VA, pp. 1-6,2015.
[17] J. Mantyjarvi, J. Koivumaki and P. Vuori, “Keystroke recognition for virtual keyboard,” IEEE International Conference on Multimedia and Expo, Vol.2, pp. 429-432 2002.
[18] S. Ravindran, C. Gautam and A. Tiwari, “Keystroke user recognition through extreme learning machine and evolving cluster method,” IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, pp. 1-5,2015.