An Automated Fingerprint Image Detection and Localization Approach-based Unsupervised Learning Algorithms using Low-quality Biometrics Plam Data

Abdulrasool Jadaan Abed - Department of Computer Science, College of Sciences, University of Diyala, Iraq
Dhahir Abdulhadi Abdullah - Department of Computer Science, College of Sciences, University of Diyala, Iraq


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.3-2.1665

Abstract


In this study, fingerprint identification and classification of low-quality fingerprints have been analyzed accordingly.  As technology advances and methodologies evolve, staying at the forefront of research and innovation is imperative. The challenges addressed in this paper provide a foundation for future investigations and underscore the importance of developing resilient and adaptable biometric systems for real-world applications. The quest for accurate, efficient, and robust fingerprint identification in adverse conditions is a testament to the continuous evolution and refinement of machine learning and deep learning approaches in biometrics. While deep learning models exhibited improved performance, it is essential to acknowledge the need for further research and development in this domain. Additionally, integrating multimodal biometric systems and combining fingerprint data with other biometric modalities might present a viable avenue for mitigating the limitations associated with degraded fingerprints. In this paper, we develop a fingerprint identification approach for low-quality fingerprint images. The success rate accuracy of the propped algorithm for the low-quality fingerprint images should be significantly better than that of the standard local minutia approach. The main design of our deep learning approach is based on detecting and extracting the primary correlation during the training and using the correlation feature map to calculate the distance between the low-quality fingerprint images during the predicting phase. The experimental results show a very promising repulsing and high prediction accuracy.


Keywords


Fingerprint; low quality; deformed fingerprint; deep learning

Full Text:

PDF

References


W. Li, J. You, and D. Zhang, “Texture-based palmprint retrieval using a layered search scheme for personal identification,” IEEE Transactions on Multimedia, vol. 7, no. 5, pp. 891–898, Oct. 2005, doi: 10.1109/tmm.2005.854380.

A. K. Jain, R. Bolle, and S. Pankanti, Biometrics: Personal Identification in Networked Society. Norwell, MA, USA: Kluwer, 1999.

M. Ye, J. Shen, G. Lin, T. Xiang, L. Shao, and S. C. H. Hoi, “Deep Learning for Person Re-Identification: A Survey and Outlook,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 6, pp. 2872–2893, Jun. 2022, doi: 10.1109/tpami.2021.3054775.

M. A. Ferrag, L. Maglaras, A. Derhab, and H. Janicke, “Authentication schemes for smart mobile devices: threat models, countermeasures, and open research issues,” Telecommunication Systems, vol. 73, no. 2, pp. 317–348, Sep. 2019, doi:10.1007/s11235-019-00612-5.

R. Clarke, “Human Identification in Information Systems,” Information Technology & People, vol. 7, no. 4, pp. 6–37, Dec. 1994, doi: 10.1108/09593849410076799.

C. Pope, "Biometric data collection in an unprotected world: Exploring the need for federal legislation protecting biometric data", J. Law Policy, vol. 26, no. 2, pp. 769, 2018.

T. Dunstone and N. Yager, Eds., Biometric System and Data Analysis. Springer US, 2009. doi: 10.1007/978-0-387-77627-9.

R. de Luis-Garcı́a, C. Alberola-López, O. Aghzout, and J. Ruiz-Alzola, “Biometric identification systems,” Signal Processing, vol. 83, no. 12, pp. 2539–2557, Dec. 2003, doi:10.1016/j.sigpro.2003.08.001.

J. Bringer, H. Chabanne, and B. Kindarji, “Identification with encrypted biometric data,” Security and Communication Networks, vol. 4, no. 5, pp. 548–562, Apr. 2011, doi: 10.1002/sec.206.

H.-K. Song et al., “Deep user identification model with multiple biometric data,” BMC Bioinformatics, vol. 21, no. 1, Jul. 2020, doi:10.1186/s12859-020-03613-3.

M. K. Khan, J. Zhang, and L. Tian, “Protecting Biometric Data for Personal Identification,” Advances in Biometric Person Authentication, pp. 629–638, 2004, doi: 10.1007/978-3-540-30548-4_72.

W. Shen and T. Tan, “Automated biometrics-based personal identification,” Proceedings of the National Academy of Sciences, vol. 96, no. 20, pp. 11065–11066, Sep. 1999, doi:10.1073/pnas.96.20.11065.

J. Yuan and S. Yu, “Efficient privacy-preserving biometric identification in cloud computing,” 2013 Proceedings IEEE INFOCOM, pp. 2652–2660, Apr. 2013, doi:10.1109/infcom.2013.6567073.

A. K. Jain and A. Kumar, “Biometric Recognition: An Overview,” Second Generation Biometrics: The Ethical, Legal and Social Context, pp. 49–79, 2012, doi: 10.1007/978-94-007-3892-8_3.

V. Awasthi, V. Awasthi, and K. K. Tiwari, “Finger print analysis using termination and bifurcation minutiae,” Int. J. Emerg. Technol. Adv. Eng., vol. 2, pp. 124–130, Feb. 2012.

M. Kaur, M. Singh, A. Girdhar and S. Parvinder, "Fingerprint verification system using minutiae extraction technique", World Academy of Science Engineering and Technology, vol. 46, pp. 497-502, 2008.

N. Rivaldería and E. Gutiérrez-Redomero, “Interpopulational differences in the frequency and distribution of delta types,” Forensic Science International, vol. 322, p. 110749, May 2021, doi:10.1016/j.forsciint.2021.110749.

Q. Zhao, D. Zhang, L. Zhang, and N. Luo, “Adaptive fingerprint pore modeling and extraction,” Pattern Recognition, vol. 43, no. 8, pp. 2833–2844, Aug. 2010, doi: 10.1016/j.patcog.2010.02.016.

A. K. Jain, Y. Chen, and M. Demirkus, “Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 15–27, Jan. 2007, doi: 10.1109/tpami.2007.250596.

P. Tertychnyi, C. Ozcinar, and G. Anbarjafari, “Low‐quality fingerprint classification using deep neural network,” IET Biometrics, vol. 7, no. 6, pp. 550–556, Oct. 2018, doi: 10.1049/iet-bmt.2018.5074.

S. Kriangkhajorn, K. Horapong, and V. Areekul, “Spectral Filter Predictor for Progressive Latent Fingerprint Restoration,” IEEE Access, vol. 12, pp. 66773–66800, 2024, doi:10.1109/access.2024.3397729.

H. Byeon et al., “Artificial intelligence-Enabled deep learning model for multimodal biometric fusion,” Multimedia Tools and Applications, vol. 83, no. 33, pp. 80105–80128, Feb. 2024, doi:10.1007/s11042-024-18509-0.

S. K. Gupta, A. Alemran, C. P. Ranjith, and M. S. K. Mohideen, “Reliable Fingerprint Classification Based on Novel Deep Learning Approach,” Advancements in Science and Technology for Healthcare, Agriculture, and Environmental Sustainability, pp. 353–360, Apr. 2024, doi: 10.1201/9781032708348-54.

V Kakulapati, S Subhani, D Madireddy, R Mounika and A Bhargavi, “Fingerprint Recognition Using the HoG and Lime Algorithm,” Acta Scientific Computer Sciences, vol. 6, no. 6, 2024.

A. Tarek Mahmoud et al., “An Automatic Deep Neural Network Model for Fingerprint Classification,” Intelligent Automation & Soft Computing, vol. 36, no. 2, pp. 2007–2023, 2023, doi:10.32604/iasc.2023.031692.

K. Anusha and P. V. Siva Kumar, “Fingerprint Image Enhancement for Crime Detection Using Deep Learning,” Proceedings of the International Conference on Cognitive and Intelligent Computing, pp. 257–268, 2023, doi: 10.1007/978-981-19-2358-6_25.

T. S. Kishore and S. M. Kumar, “Efficient anti-scamming strategy for fingerprint identification using novel enhanced patch based deep learning,” The 4TH International Conference on Material Science and Applications, vol. 2822, p. 020059, 2023, doi:10.1063/5.0172962.

T. A. Chmielewski & P. R. Kalata, “Biometric Techniques: The Fundamentals of Evaluation”. NIST Special Publication SP, pp. 335-342, 2001.

K. Saeed, “Biometrics Principles and Important Concerns,” Biometrics and Kansei Engineering, pp. 3–20, 2012, doi:10.1007/978-1-4614-5608-7_1.

N. V. Boulgouris, Konstantinos N. Plataniotis and Evangelia Micheli- Tzanakou, "PersonSpecific Characteristic Feature Selection for Face Recognition", Biometrics: Theory Methods and Applications, pp. 113-141, 2010.