Application of Machine Learning in Fingerprint Image Enhancement and Recognition: A Review
Keywords:Fingerprint, Pre-processing, Image Enhancement, Machine Learning, Matching.
Biometric characteristics helps to recognize an individual among others. Each individual has a unique biometric feature. So, an automated system is designed to recognize an individual. In today’s growing AI development, biometric recognition is applied in many security systems. One of oldest and widely used authentic biometric methodology is fingerprint recognition. Many fingerprint recognition algorithms are designed and developed in order to reduce error rate and to improve accuracy. In this paper, a comprehensive review is presented on various techniques used for fingerprint recognition system along with their performance and their limitations. The purpose of this paper is to review various recent work on the fingerprint recognition system, to explain step by step the steps for recognizing fingerprints, and to provide summaries of the fingerprint databases with functionality
Mouad .M.H.Ali, Vivek H. Mahale “Overview of Fingerprint Recognition System” International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 1334- 1338, 2016.
Umesh Singh Tomar, Abhinav Vidwans “A Review of Fingerprint Recognition by Minutiae’s Analysis” International Journal of Engineering and Information Systems (IJEAIS), Vol. 1 Issue 8, pp. 182-185, October 2017.
Priyanka Rani, IIPinki Sharma “A Review Paper on Fingerprint Identification System” International Journal of Advanced Research in Computer Science & Technology (IJARCST 2014) Vol. 2, Issue 3 July - Sept. 2014.
Ritu, Matish Garg “A Review on Fingerprint-Based Identification System” International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 3, March 2014.
Cynthia D’Souza N , Leeda Jovita Rodrigues “A Survey On Fingerprint Recognition Techniques” International Journal of Latest Trends in Engineering and Technology Special Issue SACAIM, pp. 441-447, 2016.
R. Kumar, B.R.D. Vikram, “Fingerprint matching using multidimensional ann”, Eng. Appl. Artif. Intell. Vol. 23 pp. 222–228, 2010. DOI: https://doi.org/10.1016/j.engappai.2009.11.005
Khalil-Hani, Mohamed, Muhammad N. Marsono, and Rabia Bakhteri. "Biometric encryption based on a fuzzy vault scheme with a fast chaff generation algorithm." Future Generation Computer Systems vol. 29, issue 3 pp. 800-810, 2013. DOI: https://doi.org/10.1016/j.future.2012.02.002
Jing-Wein Wang, Ngoc Tuyen Le, Chou-Chen Wang, and Jiann-Shu Lee, “Enhanced Ridge Structure For Improving Fingerprint Image Quality Based On A Wavelet Domain,” IEEE Signal Processing Letters, Vol. 22, No. 4, pp. 390-395, April 2015. DOI: https://doi.org/10.1109/LSP.2014.2361212
AlaBalti , MounirSayadi and Farhat Fnaiech, “Supervised Neural Network And Minimum Distance Features Between Singularities For Fingerprint Verification,” 2013 10th IEEE International MultiConference On Systems, Signals & Devices (Ssd) Hammamet, Tunisia, March 18-21, 2013. DOI: https://doi.org/10.1109/SSD.2013.6564001
M. Selvi and Aloysius George, “FBFET: Fuzzy Based Fingerprint Enhancement Technique based on Adaptive Thresholding,” ICCCNT – 2013. DOI: https://doi.org/10.1109/ICCCNT.2013.6726776
Gang Cao, Yao Zhao, Rongrong Ni, and Xuelong Li, “Contrast Enhancement-Based ForensicsIn Digital Images”, IEEE Transactions On Information Forensics And Security, Vol. 9, No. 3, pp. 515-526, March 2014. DOI: https://doi.org/10.1109/TIFS.2014.2300937
Leandra Webb and Mmamolatelo Mathekga, “Towards A Complete Rule-Based Classification Approach for Flat Fingerprints,” IEEE Second International Symposium On Computing And Networking, 978- 1-4799-4152-0/14, pp. 549-556, 2014.
P. Tertychnyi, C. Ozcinar and G. Anbarjafari, "Low-quality fingerprint classification using deep neural network," in IET Biometrics, vol. 7, no. 6, pp. 550-556, 11 2018. DOI: https://doi.org/10.1049/iet-bmt.2018.5074
K. Han, Z. Wang and Z. Chen, "Fingerprint Image Enhancement Method based on Adaptive Median Filter," 2018 24th Asia-Pacific Conference on Communications (APCC), Ningbo, China, 2018, pp. 40-44. DOI: https://doi.org/10.1109/APCC.2018.8633498
Y. Zhang, D. Shi, X. Zhan, D. Cao, K. Zhu and Z. Li, "Slim-ResCNN: A Deep Residual Convolutional Neural Network for Fingerprint Liveness Detection," in IEEE Access, vol. 7, pp. 91476-91487, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2927357
A. Muhammed and A. R. Pais, "A Novel Fingerprint Image Enhancement based on Super Resolution," International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2020, pp. 165-170. DOI: https://doi.org/10.1109/ICACCS48705.2020.9074196
How to Cite
Copyright (c) 2021 Kshitij Singh, Dr. Gireesh Kumar Dixit
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJOSCIENCE follows an Open Journal Access policy. Authors retain the copyright of the original work and grant the rights of publication to the publisher with the work simultaneously licensed under a Creative Commons CC BY License that allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Authors are permitted to post their work in institutional repositories, social media or other platforms.
Under the following terms:
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.