Parmak İzi Kimliklendirmesinde Yapay Zeka Uygulamaları: Geleneksel Yöntemlerden Akıllı Sistemlere

Morgan RM. Forensic science. The importance of identity in theory and practice. Forensic Science International. 2019:1;1(1).239–242. doi:10.1016/j.fsisyn.2019.09.001

Jain AK, Nandakumar K, Ross A. 50 years of biometric research: Accomplishments, challenges, and opportunities. Pattern Recognit Letters. 2016;1;79:80–105. doi:10.1016/j.patrec.2015.12.013

Jain AK, Kumar A. Biometric recognition: An overview. In: Mordini E, Tzovaras D. (eds) International Library of Ethics, Law and Technology. Vol. 11, Dordrecht: Springer; 2012. p. 49–79. doi:10.1007/978-94-007-3892-8_3 Availabe from: https://link.springer.com/chapter/10.1007/978-94-007-3892-8_3. (Accessed 15th March 2026).

Kücken M, Newell AC. Fingerprint formation. Journal of Theoretical Biology. 2005;7;235(1):71–83. doi:10.1016/j.jtbi.2004.12.020 PubMed PMID: 15833314.

Bleay SM, Croxton RS, de Puit. Fingerprint development techniques : theory and application. 1st ed. Chichester:Wiley; 2018.

Neumann C, Stern H. Forensic examination of fingerprints: past, present, and future. CHANCE. 2016;2;29(1):9–16. doi:10.1080/09332480.2016.1156353

Kuriyal S. Role of physical evidences in crime scene investigation: An analysis. International Journal of Humanities and Social Science Invention. 2025;14(6):37–43. doi:10.35629/7722-14063743

Shakeel M, Syed SK. A review on fingerprint as an identification tool in the discipline of forensics. Forensic Insights and Health Sciences Bulletin. 2023;31;1(2):38–43. doi:10.56770/fi2023112

Deshpande UU, Malemath VS. A study on automatic latent fingerprint identification system. Journal of Computer Science Research. 2022;28;4(1):38–50. doi:10.30564/jcsr.v4i1.4388

Singla N, Kaur M, Sofat S. Automated latent fingerprint identification system: A review. Forensic Science International. 2020;1;309(1):110187. doi:10.1016/j.forsciint.2020.110187 PubMed PMID: 32163854.

Daluz HM. Fundamentals of fingerprint analysis. 2nd ed. Boca Raton, FL: CRC Press; 2018. doi:https://doi.org/10.4324/9781351043205

Payne-James J, Jones R. Identification of the living and the dead. In: Payne-James J, Jones R (eds.) Simpson’s forensic medicine. Boca Raton: CRC Press; 2019. p. 198–207. doi:10.1201/9781315157054-14

Jain AK, Feng J, Nandakumar K. Fingerprint matching. Computer. 2010;43(2):36–44. doi:10.1109/MC.2010.38

Hefetz I. Integrating AI systems in criminal justice: The forensic expert as a corridor between algorithms and courtroom evidence. Forensic Sciences, Vol 5. 2025:27;5(4). doi:10.3390/forensicsci5040053

Ramírez-Sáyago E, Loyola-González O, Medina-Pérez MA. Towards inpainting and denoising latent fingerprints: A study on the impact in latent fingerprint identification. In: Figueroa Mora K, Anzurez Marín J, Cerda J, et al. (eds). Pattern Recognition. Springer, Cham; 2020. p. 76–86. doi:10.1007/978-3-030-49076-8_8 Available from: https://link.springer.com/chapter/10.1007/978-3-030-49076-8_8 (Accessed 11th March 2026).

Alonso-Fernandez F, Fierrez J. Fingerprint databases and evaluation. Encyclopedia of Biometrics. 2015:1;599–606. doi:10.1007/978-1-4899-7488-4_61

Gupta R, Khari M, Gupta D, et al. Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction. Information Sciences. 2020:1;530(4):201–18. doi:10.1016/j.ins.2020.01.031

Lan S, Guo Z, You J. Pre-registration of translated/distorted fingerprints based on correlation and the orientation field. Information Sciences. 2020:1;520:292–304. doi:10.1016/j.ins.2020.02.017

Alam NA, Ahsan M, Based MA, et al. An intelligent system for automatic fingerprint identification using feature fusion by Gabor filter and deep learning. Computers and Electrical Engineering. 2021:1;95(6):107387. doi:10.1016/j.compeleceng.2021.107387

Yang W, Wang S, Hu J, et al. Security and accuracy of fingerprint-based biometrics: A review. Symmetry 2019, Vol 11. 28;11(2). doi:10.3390/sym11020141

Herke C. Automated fingerprint identification: The role of artificial intelligence in crime scene investigation. Forensic Sciences, Vol 6. 2026:22;6(1):6. doi:10.3390/forensicsci6010006

Scientific Working Group on Friction Ridge Analysis, Study and Technology (SWGFAST). Standards for Examining Friction Ridge Impressions and Resulting Conclusions (Latent/Tenprint). 2013. Available from: https://www.nist.gov/system/files/documents/2016/10/26/swgfast_examinations-conclusions_2.0_130427.pdf. (Accessed 12th March 2026).

Dror IE, Charlton D. Why experts make errors. Journal of Forensic Identification. 2006;56(4):600–16.

Cole SA. Forensics without uniqueness, conclusions without individualization: the new epistemology of forensic identification. Law, Probability and Risk. 2009:1;8(3):233–55. doi:10.1093/lpr/mgp016

Morrison GS. Advancing a paradigm shift in evaluation of forensic evidence: The rise of forensic data science. Forensic Science International. 2022:1;5:100270. doi:10.1016/j.fsisyn.2022.100270 PubMed PMID: 35634572.

Maltoni D, Maio D, Jain AK, et al. Handbook of fingerprint recognition. 2nd ed. London;Springer. 2009. doi:10.1007/978-1-84882-254-2

Komarinski P. Automated fingerprint identification systems (AFIS). 1st ed. San Diego:Academic Press; 2004.

Jaiswal P, Koner A, Namboodiri AM. Advancing fingerprint recognition quality assessment: Introducing the FRBQ metric for enhanced fingerprint recognition. In: ICVGIP '23: Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing. 15 Dec 2023, Rupnagar India. doi:10.1145/3627631.3627649.

Tom KR, Knorr KB, Davis CE. Next Generation Identification system: Latent print matching algorithm and casework practices. Forensic Science International. 2022:1;332(19):111180. doi:10.1016/j.forsciint.2022.111180 PubMed PMID: 35063814.

Zhou R, Zhong D, Han J. Fingerprint ıdentification using SIFT-based minutia descriptors and improved all descriptor-pair matching. Sensors. Vol 13, 2013:6;13(3):3142–3156. doi:10.3390/s130303142

Langenburg G, Hall C, Rosemarie Q. Utilizing AFIS searching tools to reduce errors in fingerprint casework. Forensic Science International. 2015:1;257:123–33. doi:10.1016/j.forsciint.2015.07.054

Efrizoni L, Armoogum S, Zakaria MZ. Deep learning innovations in fingerprint recognition: A comparative study of model efficiencies. International Journal of Advances in Artificial Intelligence and Machine Learning. 2024:6;1(1):28–35. doi:10.58723/ijaaiml.v1i1.294

Peng A, Huang R. Research progress on the application of deep learning in fingerprint recognition. Pattern Recognition. 2026:1;171:112216. doi:10.1016/j.patcog.2025.112216

Ranjan A, Prakash N, Peddi S, et al. A Novel Framework for Robust Fingerprint Representations using Deep Convolution Network with Attention Mechanism. In: ICVGIP '23: Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing. 15 Dec 2023, Rupnagar India. doi:10.1145/3627631.3627651

Naim NF, Yassin AIM, Zakaria NB. Classification of thumbprint using Artificial Neural Network (ANN). In: Proceedings of the 2011 IEEE International Conference on System Engineering and Technology. (ICSET 2011). Shah Alam, Malaysia. 27-28 June 2011. doi:10.1109/ICSEngT.2011.5993456

Marák P, Hambalík A. Fingerprint recognition system using artificial neural network as feature extractor: Design and performance evaluation. Tatra Mountains Mathematical Publications. 2016:1;67(1):117–34. doi:10.1515/tmmp-2016-0035

Singh R, Singh R, Tripathi RK, et al. Fingerprint recognition using artificial neural networks. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences. 95:2025:19;95(2):127–35. doi:10.1007/s40010-025-00917-y

Jea TY, Govindaraju V. A minutia-based partial fingerprint recognition system. Pattern Recognition. 2005;38(10):1672–84. doi:10.1016/j.patcog.2005.03.016

Pawar T. Fingerprint image classification and retrieval using statistical methods [Doctoral dissertation]. Gujarat Technological University; 2018.

Shah S, Tembhurne J. Object detection using convolutional neural networks and transformer-based models: a review. Journal of Electrical Systems and Information Technology 2023;10:1. 20;10(1):54. doi:10.1186/s43067-023-00123-z

Zaman MF, Adedayo OM, Liu Q. Hybrid Fingerprint Classification Using Deep Learning and Sobel Feature Fusion. In: ETNCC 2025: Proceedings of the 2025 International Conference on Emerging Trends in Networks and Computer Communications. 5-7 August 2025. Windhoek, Namibia. doi:10.1109/etncc66224.2025.11299795

Abed JA, Abdulah AD. Advancements and challenges in low-quality fingerprint ıdentification: A comprehensive survey. Bilad Alrafidain Journal for Engineering Science and Technology. 2025:15;4(1):127–36. doi:10.56990/bajest/2025.040111

Ghalb H, Khalaf ZA. A survey of fingerprint identification system using deep learning. International Journal of Computing and Digital Systems. 2025;17(1). doi:10.12785/ijcds/1571022983

Lisha PP, Jayasree VK. Enhancing fingerprint image resolution using auto-encoder and interpolation techniques. International Journal of Electronics and Communication Engineering. 2024:30;11(4):102–14. doi:10.14445/23488549/IJECE-V11I4P111

Jainy Jacob M, Shanmugapriya D. A deep generative recognition framework for low-quality and partial fingerprints. In: Proceedings of 3rd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2025 Institute of Electrical and Electronics Engineers Inc. 6-8 Aug 2025. Erode, India doi:10.1109/ICSCDS65426.2025.11167726

Van Tilborg HC, Jajodia S. Encyclopedia of Cryptography and Security. van Tilborg HCA, Jajodia S (eds). 2nd ed. Boston, MA: Springer US; 2011. doi:10.1007/978-1-4419-5906-5

Ali M, Wang C, Ahmad MO. A deep CNN-based feature extraction and matching of pores for fingerprint recognition. IEEE Transactions on Biometrics, Behavior, and Identity Science. 7;3:2025. p. 368–383. doi:10.1109/TBIOM.2024.3516634

Moses KR, Higgins P, Mccabe M, et al. Automated fingerprint identification system (AFIS). In: Holder EH, Robinson LO, Laub JH (eds.) The fingerprint sourcebook. 1st ed. Washington: U.S. Dept. of Justice Office of Justice Programs. 2011.

Chegur P, Patil N, Doddamani N, et al. Separation of overlapped fingerprint ımages using deep learning. In: IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023 - Proceedings. 19-21 April 2023. Bangalore, India. doi:10.1109/ICAECIS58353.2023.10169966

Wahab A, Khan TM, Iqbal S, et al. Latent fingerprint enhancement for accurate minutiae detection. Procedia Computure Sciences. 2024:18;246(C):1558–67. doi:10.1016/j.procs.2024.09.722

Shukla RK, Kumar R, Buhari A. An efficient approach to fingerprint recognition with proposed CNN. In: 2025 International Conference on Intelligent and Secure Engineering Solutions (CISES). Institute of Electrical and Electronics Engineers (IEEE); 11-13 August 2025. Greater Noida Gautam Budh Nagar, India. doi:10.1109/cises66934.2025.11265695

Riaz I, Ali AN, Ibrahim H. Loss of fingerprint features and recognition failure due to physiological factors- a literature survey. Multimedia Tools and Applications. 2024;19;83(39):87153–87178. doi:10.1007/s11042-024-19848-8

Kumar M, Kumar S, Gulhane M, et al. Deep neural network-based fingerprint reformation for minimizing displacement. In: Proceedings of the 2023 12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023. Institute of Electrical and Electronics Engineers Inc.; 22-23 December 2023. Moradabad, India, doi:10.1109/SMART59791.2023.10428379

Yoon S, Feng J, Jain AK. Altered fingerprints: Analysis and detection. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012;34(3). pp. 451-464. doi:10.1109/TPAMI.2011.161

Fattahi J, Mejri M. Damaged fingerprint recognition by convolutional long short-term memory networks for forensic purposes. In: 2021 IEEE 5th International Conference on Cryptography, Security and Privacy, CSP 2021 Institute of Electrical and Electronics Engineers Inc.; Zhuhai, China, 08-10 January 2021 pp. 193–199. doi:10.1109/CSP51677.2021.9357588

Fattahi J, Lakdher BE, Mejri M, et al. FingFor: A deep learning tool for biometric forensics. In: 10th 2024 International Conference on Control, Decision and Information Technologies, CoDIT 2024. Vallette, Malta, 01-04 July 2024;1667–1672. doi:10.1109/CoDIT62066.2024.10708215

Abdullah DA, Hamad DR, Ibrahim BR, et al. Innovative deep learning architecture for enhanced altered fingerprint recognition. arXiv preprint arXiv:2509.20537. Available from: http://arxiv.org/abs/2509.20537 (Accessed: 25/02/2026).

Lakshmi BN, Shankar Gowda BN, Krithika M, Decentralized AI-driven forensic fingerprint recognition system. In: Roceedings of the 9th International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS). Institute of Electrical and Electronics Engineers (IEEE); Bangalore, India. 20-22 November 2025. doi:10.1109/csitss67709.2025.11295413

ANSI/ASB. Standard for friction ridge examination conclusions. ANSI/ASB Standard 013-25. 1st ed. Academy Standards Board; 2025.

Indovina M, Dvornychenko V, Hicklin RA, et al. ELFT-EFS: Evaluation of latent fingerprint technologies: Extended feature sets. NIST Interagency Report 7859. National Institute of Standards and Technology; 2012.

OSAC Friction Ridge Subcommittee. Technical report for task-relevant ınformation in friction ridge examination. OSAC 2023-S-0026. National Institute of Standards and Technology; 2025.

Morgan RM. Forensic science. The importance of identity in theory and practice. Forensic Science International. 2019:1;1(1).239–242. doi:10.1016/j.fsisyn.2019.09.001

Jain AK, Nandakumar K, Ross A. 50 years of biometric research: Accomplishments, challenges, and opportunities. Pattern Recognit Letters. 2016;1;79:80–105. doi:10.1016/j.patrec.2015.12.013

Jain AK, Kumar A. Biometric recognition: An overview. In: Mordini E, Tzovaras D. (eds) International Library of Ethics, Law and Technology. Vol. 11, Dordrecht: Springer; 2012. p. 49–79. doi:10.1007/978-94-007-3892-8_3 Availabe from: https://link.springer.com/chapter/10.1007/978-94-007-3892-8_3. (Accessed 15th March 2026).

Kücken M, Newell AC. Fingerprint formation. Journal of Theoretical Biology. 2005;7;235(1):71–83. doi:10.1016/j.jtbi.2004.12.020 PubMed PMID: 15833314.

Bleay SM, Croxton RS, de Puit. Fingerprint development techniques : theory and application. 1st ed. Chichester:Wiley; 2018.

Neumann C, Stern H. Forensic examination of fingerprints: past, present, and future. CHANCE. 2016;2;29(1):9–16. doi:10.1080/09332480.2016.1156353

Kuriyal S. Role of physical evidences in crime scene investigation: An analysis. International Journal of Humanities and Social Science Invention. 2025;14(6):37–43. doi:10.35629/7722-14063743

Shakeel M, Syed SK. A review on fingerprint as an identification tool in the discipline of forensics. Forensic Insights and Health Sciences Bulletin. 2023;31;1(2):38–43. doi:10.56770/fi2023112

Deshpande UU, Malemath VS. A study on automatic latent fingerprint identification system. Journal of Computer Science Research. 2022;28;4(1):38–50. doi:10.30564/jcsr.v4i1.4388

Singla N, Kaur M, Sofat S. Automated latent fingerprint identification system: A review. Forensic Science International. 2020;1;309(1):110187. doi:10.1016/j.forsciint.2020.110187 PubMed PMID: 32163854.

Daluz HM. Fundamentals of fingerprint analysis. 2nd ed. Boca Raton, FL: CRC Press; 2018. doi:https://doi.org/10.4324/9781351043205

Payne-James J, Jones R. Identification of the living and the dead. In: Payne-James J, Jones R (eds.) Simpson’s forensic medicine. Boca Raton: CRC Press; 2019. p. 198–207. doi:10.1201/9781315157054-14

Jain AK, Feng J, Nandakumar K. Fingerprint matching. Computer. 2010;43(2):36–44. doi:10.1109/MC.2010.38

Hefetz I. Integrating AI systems in criminal justice: The forensic expert as a corridor between algorithms and courtroom evidence. Forensic Sciences, Vol 5. 2025:27;5(4). doi:10.3390/forensicsci5040053

Ramírez-Sáyago E, Loyola-González O, Medina-Pérez MA. Towards inpainting and denoising latent fingerprints: A study on the impact in latent fingerprint identification. In: Figueroa Mora K, Anzurez Marín J, Cerda J, et al. (eds). Pattern Recognition. Springer, Cham; 2020. p. 76–86. doi:10.1007/978-3-030-49076-8_8 Available from: https://link.springer.com/chapter/10.1007/978-3-030-49076-8_8 (Accessed 11th March 2026).

Alonso-Fernandez F, Fierrez J. Fingerprint databases and evaluation. Encyclopedia of Biometrics. 2015:1;599–606. doi:10.1007/978-1-4899-7488-4_61

Gupta R, Khari M, Gupta D, et al. Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction. Information Sciences. 2020:1;530(4):201–18. doi:10.1016/j.ins.2020.01.031

Lan S, Guo Z, You J. Pre-registration of translated/distorted fingerprints based on correlation and the orientation field. Information Sciences. 2020:1;520:292–304. doi:10.1016/j.ins.2020.02.017

Alam NA, Ahsan M, Based MA, et al. An intelligent system for automatic fingerprint identification using feature fusion by Gabor filter and deep learning. Computers and Electrical Engineering. 2021:1;95(6):107387. doi:10.1016/j.compeleceng.2021.107387

Yang W, Wang S, Hu J, et al. Security and accuracy of fingerprint-based biometrics: A review. Symmetry 2019, Vol 11. 28;11(2). doi:10.3390/sym11020141

Herke C. Automated fingerprint identification: The role of artificial intelligence in crime scene investigation. Forensic Sciences, Vol 6. 2026:22;6(1):6. doi:10.3390/forensicsci6010006

Scientific Working Group on Friction Ridge Analysis, Study and Technology (SWGFAST). Standards for Examining Friction Ridge Impressions and Resulting Conclusions (Latent/Tenprint). 2013. Available from: https://www.nist.gov/system/files/documents/2016/10/26/swgfast_examinations-conclusions_2.0_130427.pdf. (Accessed 12th March 2026).

Dror IE, Charlton D. Why experts make errors. Journal of Forensic Identification. 2006;56(4):600–16.

Cole SA. Forensics without uniqueness, conclusions without individualization: the new epistemology of forensic identification. Law, Probability and Risk. 2009:1;8(3):233–55. doi:10.1093/lpr/mgp016

Morrison GS. Advancing a paradigm shift in evaluation of forensic evidence: The rise of forensic data science. Forensic Science International. 2022:1;5:100270. doi:10.1016/j.fsisyn.2022.100270 PubMed PMID: 35634572.

Maltoni D, Maio D, Jain AK, et al. Handbook of fingerprint recognition. 2nd ed. London;Springer. 2009. doi:10.1007/978-1-84882-254-2

Komarinski P. Automated fingerprint identification systems (AFIS). 1st ed. San Diego:Academic Press; 2004.

Jaiswal P, Koner A, Namboodiri AM. Advancing fingerprint recognition quality assessment: Introducing the FRBQ metric for enhanced fingerprint recognition. In: ICVGIP '23: Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing. 15 Dec 2023, Rupnagar India. doi:10.1145/3627631.3627649.

Tom KR, Knorr KB, Davis CE. Next Generation Identification system: Latent print matching algorithm and casework practices. Forensic Science International. 2022:1;332(19):111180. doi:10.1016/j.forsciint.2022.111180 PubMed PMID: 35063814.

Zhou R, Zhong D, Han J. Fingerprint ıdentification using SIFT-based minutia descriptors and improved all descriptor-pair matching. Sensors. Vol 13, 2013:6;13(3):3142–3156. doi:10.3390/s130303142

Langenburg G, Hall C, Rosemarie Q. Utilizing AFIS searching tools to reduce errors in fingerprint casework. Forensic Science International. 2015:1;257:123–33. doi:10.1016/j.forsciint.2015.07.054

Efrizoni L, Armoogum S, Zakaria MZ. Deep learning innovations in fingerprint recognition: A comparative study of model efficiencies. International Journal of Advances in Artificial Intelligence and Machine Learning. 2024:6;1(1):28–35. doi:10.58723/ijaaiml.v1i1.294

Peng A, Huang R. Research progress on the application of deep learning in fingerprint recognition. Pattern Recognition. 2026:1;171:112216. doi:10.1016/j.patcog.2025.112216

Ranjan A, Prakash N, Peddi S, et al. A Novel Framework for Robust Fingerprint Representations using Deep Convolution Network with Attention Mechanism. In: ICVGIP '23: Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing. 15 Dec 2023, Rupnagar India. doi:10.1145/3627631.3627651

Naim NF, Yassin AIM, Zakaria NB. Classification of thumbprint using Artificial Neural Network (ANN). In: Proceedings of the 2011 IEEE International Conference on System Engineering and Technology. (ICSET 2011). Shah Alam, Malaysia. 27-28 June 2011. doi:10.1109/ICSEngT.2011.5993456

Marák P, Hambalík A. Fingerprint recognition system using artificial neural network as feature extractor: Design and performance evaluation. Tatra Mountains Mathematical Publications. 2016:1;67(1):117–34. doi:10.1515/tmmp-2016-0035

Singh R, Singh R, Tripathi RK, et al. Fingerprint recognition using artificial neural networks. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences. 95:2025:19;95(2):127–35. doi:10.1007/s40010-025-00917-y

Jea TY, Govindaraju V. A minutia-based partial fingerprint recognition system. Pattern Recognition. 2005;38(10):1672–84. doi:10.1016/j.patcog.2005.03.016

Pawar T. Fingerprint image classification and retrieval using statistical methods [Doctoral dissertation]. Gujarat Technological University; 2018.

Shah S, Tembhurne J. Object detection using convolutional neural networks and transformer-based models: a review. Journal of Electrical Systems and Information Technology 2023;10:1. 20;10(1):54. doi:10.1186/s43067-023-00123-z

Zaman MF, Adedayo OM, Liu Q. Hybrid Fingerprint Classification Using Deep Learning and Sobel Feature Fusion. In: ETNCC 2025: Proceedings of the 2025 International Conference on Emerging Trends in Networks and Computer Communications. 5-7 August 2025. Windhoek, Namibia. doi:10.1109/etncc66224.2025.11299795

Abed JA, Abdulah AD. Advancements and challenges in low-quality fingerprint ıdentification: A comprehensive survey. Bilad Alrafidain Journal for Engineering Science and Technology. 2025:15;4(1):127–36. doi:10.56990/bajest/2025.040111

Ghalb H, Khalaf ZA. A survey of fingerprint identification system using deep learning. International Journal of Computing and Digital Systems. 2025;17(1). doi:10.12785/ijcds/1571022983

Lisha PP, Jayasree VK. Enhancing fingerprint image resolution using auto-encoder and interpolation techniques. International Journal of Electronics and Communication Engineering. 2024:30;11(4):102–14. doi:10.14445/23488549/IJECE-V11I4P111

Jainy Jacob M, Shanmugapriya D. A deep generative recognition framework for low-quality and partial fingerprints. In: Proceedings of 3rd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2025 Institute of Electrical and Electronics Engineers Inc. 6-8 Aug 2025. Erode, India doi:10.1109/ICSCDS65426.2025.11167726

Van Tilborg HC, Jajodia S. Encyclopedia of Cryptography and Security. van Tilborg HCA, Jajodia S (eds). 2nd ed. Boston, MA: Springer US; 2011. doi:10.1007/978-1-4419-5906-5

Ali M, Wang C, Ahmad MO. A deep CNN-based feature extraction and matching of pores for fingerprint recognition. IEEE Transactions on Biometrics, Behavior, and Identity Science. 7;3:2025. p. 368–383. doi:10.1109/TBIOM.2024.3516634

Moses KR, Higgins P, Mccabe M, et al. Automated fingerprint identification system (AFIS). In: Holder EH, Robinson LO, Laub JH (eds.) The fingerprint sourcebook. 1st ed. Washington: U.S. Dept. of Justice Office of Justice Programs. 2011.

Chegur P, Patil N, Doddamani N, et al. Separation of overlapped fingerprint ımages using deep learning. In: IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023 - Proceedings. 19-21 April 2023. Bangalore, India. doi:10.1109/ICAECIS58353.2023.10169966

Wahab A, Khan TM, Iqbal S, et al. Latent fingerprint enhancement for accurate minutiae detection. Procedia Computure Sciences. 2024:18;246(C):1558–67. doi:10.1016/j.procs.2024.09.722

Shukla RK, Kumar R, Buhari A. An efficient approach to fingerprint recognition with proposed CNN. In: 2025 International Conference on Intelligent and Secure Engineering Solutions (CISES). Institute of Electrical and Electronics Engineers (IEEE); 11-13 August 2025. Greater Noida Gautam Budh Nagar, India. doi:10.1109/cises66934.2025.11265695

Riaz I, Ali AN, Ibrahim H. Loss of fingerprint features and recognition failure due to physiological factors- a literature survey. Multimedia Tools and Applications. 2024;19;83(39):87153–87178. doi:10.1007/s11042-024-19848-8

Kumar M, Kumar S, Gulhane M, et al. Deep neural network-based fingerprint reformation for minimizing displacement. In: Proceedings of the 2023 12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023. Institute of Electrical and Electronics Engineers Inc.; 22-23 December 2023. Moradabad, India, doi:10.1109/SMART59791.2023.10428379

Yoon S, Feng J, Jain AK. Altered fingerprints: Analysis and detection. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012;34(3). pp. 451-464. doi:10.1109/TPAMI.2011.161

Fattahi J, Mejri M. Damaged fingerprint recognition by convolutional long short-term memory networks for forensic purposes. In: 2021 IEEE 5th International Conference on Cryptography, Security and Privacy, CSP 2021 Institute of Electrical and Electronics Engineers Inc.; Zhuhai, China, 08-10 January 2021 pp. 193–199. doi:10.1109/CSP51677.2021.9357588

Fattahi J, Lakdher BE, Mejri M, et al. FingFor: A deep learning tool for biometric forensics. In: 10th 2024 International Conference on Control, Decision and Information Technologies, CoDIT 2024. Vallette, Malta, 01-04 July 2024;1667–1672. doi:10.1109/CoDIT62066.2024.10708215

Abdullah DA, Hamad DR, Ibrahim BR, et al. Innovative deep learning architecture for enhanced altered fingerprint recognition. arXiv preprint arXiv:2509.20537. Available from: http://arxiv.org/abs/2509.20537 (Accessed: 25/02/2026).

Lakshmi BN, Shankar Gowda BN, Krithika M, Decentralized AI-driven forensic fingerprint recognition system. In: Roceedings of the 9th International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS). Institute of Electrical and Electronics Engineers (IEEE); Bangalore, India. 20-22 November 2025. doi:10.1109/csitss67709.2025.11295413

ANSI/ASB. Standard for friction ridge examination conclusions. ANSI/ASB Standard 013-25. 1st ed. Academy Standards Board; 2025.

Indovina M, Dvornychenko V, Hicklin RA, et al. ELFT-EFS: Evaluation of latent fingerprint technologies: Extended feature sets. NIST Interagency Report 7859. National Institute of Standards and Technology; 2012.

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