Parmak İzlerinin Sınıflandırılması

Maltoni D, Mario D, Jain AK, et al. Fingerprint classification and ındexing. In: Maltoni D, Mario D, Jain A, Prabhakar S (eds). Handbook of fingerprint recognition. 2nd ed. London:Springer. 2009:1.

Yager N, Amin A. Fingerprint classification: a review. Pattern Analytical Application. 2004;7:77-93.

Hutchins L. Systems of friction ridge classification. In: Holder EH Jr, Robinson L, Laub J, eds. The fingerprint sourcebook. National Institute of Justice; 2011:95-120.

Rodriguez J. South Atlantic crossings: Fingerprints, science, and the state in turn-of-the-century Argentina, The American historical review. American Historical Review. 2004;109(2):387.

Daluz HM. Fingerprint patterns and classification. In: Daluz HM, (ed). Fundamentals of Fingerprint Analysis. Boca Raton:FL:CRC Press; 2015b:43-58.

Koç F, Gül Y, Yavaş B, et al. Tek parmak izi tasnif sistemi. In: Tasnif Sistemleri Temel Eğitim Kitabı. 2005:21-78.

Bridges BC. Practical fingerprinting. NY:Funk and Wagnalls; 1963.

Hawthorne M. Classification Henry with FBI extension, NCIC, and IAFIS. In: Fingerprint Analysis and Understanding. Boca Raton:CRC Press; 2009:55-70.

Sodhi GS, Kaur J. The forgotten Indian pioneers of fingerprint science. Current Science. 2005;88(1):185-191.

Göl A. Parmak izinin yapısı. In: On parmak izi tasnif sistemi. Ankara: Kriminal Polis Laboratuvarları Dairesi Başkanlığı Yayını:7-20.

KPL Daire Başkanlığı. Parmak izlerinin sınıflandırılması. In: Sevinç A, ed. Temel parmak izi: kursiyer el kitabı. 1st ed. Ankara:International Police Training Center; 2011:33-43.

Demirci, S., Aydın, M., Koç, F., Gül, Y., Yavaş, B., Güneş, G., Demir, B., Tepecik, S., Ozcan, B., & Dutakü, M. (2010). Parmak izi temel eğitim kitabı (C. 17). KPL Daire Başkanlığı Yayını.

Olsen RD. Scott’s fingerprint mechanics. Thomas; 1978.

Federal Bureau of Justice. (1973). The science of fingerprints classification and uses. Içinde United States Department of Justice Federal Bureau of Investigation. 1973:87-97.

Wilson C, Candela G, Watson C. Neural network fingerprint classification. Journal of Artificial Neural Networks. 1993;1(2):203-228.

Daluz HM. Fingerprint comparisons. In: Daluz HM, (ed). Fundamentals of Fingerprint Analysis. Boca Raton:CRC Press; 2015a:237-248.

Sankaran A, Vatsa M, Singh R. Latent fingerprint matching: A survey. IEEE Access. 2014;2:982-1004. doi:10.1109/ACCESS.2014.2349879

Langenberg G. A performance study of the ACE-V process: a pilot study to measure the accuracy, precision, reproducibility, repeatability, and biasability of conclusions resulting from the ACE-V process. Journal of Forensic Identification. 2009;59(2):219.

Chen F, Zhou J. On the influence of fingerprint area in partial fingerprint recognition. In: Zheng WS, Sun Z, Wang Y, et al. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_13

Malathi S, Meena C. An efficient method for partial fingerprint recognition based on local binary pattern. In: 2010 International Conference on Communication Control and Computing Technologies. IEEE; 2010:569-572. doi:10.1109/ICCCCT.2010.5670775

Agrawal P, Kapoor R, Agrawal S. Partial fingerprint matching: Fusion of level 2 and level 3 features. In: 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence). IEEE; 2014:504-508. doi:10.1109/CONFLUENCE.2014.6949368

Siegel S. Dactylography, QUIP. Journal of Forensic Identification. 2014;64(4):428-429.

Scott B. Fingerprint Classification and Interpretation Simplified. 4th ed. Pearson; 2013.

Abbood A, Sulong G. Fingerprint classification techniques: A review. International Journal of Computer Science Issues (IJCSI). 2014;11(1).

Maltoni D. A tutorial on fingerprint recognition. Advanced Studies in Biometrics. 2005:43-68.

Darlow LN, Rosman B. Fingerprint minutiae extraction using deep learning. In: 2017 IEEE International Joint Conference on Biometrics (IJCB). IEEE; 2017:22-30. doi:10.1109/BTAS.2017.8272678

Liu W, Zhou L, Chen J. Face recognition based on lightweight convolutional neural networks. Information. 2021;12(5):191. doi:10.3390/info12050191

Zhu Y, Yin X, Jia X, et al. Latent fingerprint segmentation based on convolutional neural networks. In: 2017 IEEE Workshop on Information Forensics and Security (WIFS). IEEE; 2017:1-6. doi:10.1109/WIFS.2017.8267655

Stojanovic B, Marques O, Neskovic A, et al. Fingerprint ROI segmentation based on deep learning. In: 2016 24th Telecommunications Forum (TELFOR). IEEE; 2016:1-4. doi:10.1109/TELFOR.2016.7818799

Joshi I, Utkarsh A, Kothari R, et al. Sensor-invariant fingerprint roı segmentation using recurrent adversarial learning. In: 2021 International Joint Conference on Neural Networks (IJCNN). IEEE; 2021:1-8. doi:10.1109/IJCNN52387.2021.9533712

Ezeobiejesi J, Bhanu B. Latent fingerprint image segmentation using deep neural network. In: Bhanu B, Kumar A, (eds). Deep learning for biometrics. Cham: Springer International Publishing; 2017. p. 83–107. doi:10.1007/978-3-319-61657-5_4

Liu C, Zhi Z, Zhao W, et al. Research on local fingerprint ımage differential privacy protection method based on clustering algorithm and regression algorithm segmentation ımage. IEEE Access.2024;12:27127-27146. doi:10.1109/ACCESS.2024.3363494

Dai X, Liang J, Zhao Q, et al. Fingerprint Segmentation via Convolutional Neural Networks. In: Chinese Conference on Biometric Recognition. 2017:324-333. doi:10.1007/978-3-319-69923-3_35

Tandon S, Namboodiri A. Transformer based fingerprint feature extraction. In: 2022 26th International Conference on Pattern Recognition (ICPR). IEEE; 2022:870-876. doi:10.1109/ICPR56361.2022.9956435

Cao K, Nguyen DL, Tymoszek C, et al. End-to-end latent fingerprint search. IEEE Transactions on Information Forensics and Security. 2020;15:880-894. doi:10.1109/TIFS.2019.2930487

Engelsma JJ, Cao K, Jain AK. Learning a fixed-length fingerprint representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021;43(6):1981-1997. doi:10.1109/TPAMI.2019.2961349

Ametefe DS, Sarnin SS, Ali DM, et al. Fingerprint pattern classification using deep transfer learning and data augmentation. The Visual Computer. 7 April 2022. doi:10.1007/s00371-022-02437-x

Billah M. Explainable AI for digital forensics: ensuring transparency in legal evidence analysis. Journal of Forensic Science and Research. 3 July 2025:109-116. doi:10.29328/journal.jfsr.1001089

Hall SW, Sakzad A, Minagar S. A proof of concept ımplementation of explainable artificial ıntelligence (XAI) in digital forensics. In: Network and System Security: 16th International Conference. Denarau Island, Fiji, 9-12 December 2022:66-85. doi:10.1007/978-3-031-23020-2_4

Muaz M, Sajid S, Schulze T, et al. Explainable AI for correct root cause analysis of product quality in injection moulding. Journal of Manufacturing Processes. 2025;145:371-380.

Makrushin A, Kauba C, Kirchgasser S, et al. General requirements on synthetic fingerprint images for biometric authentication and forensic investigations. In: ACM Workshop on Information Hiding and Multimedia Security. 2021:93-104.

Kücken M. Models for fingerprint pattern formation. Forensic Science International. 2007;171(2-3):85-96. doi:10.1016/j.forsciint.2007.02.025

Ram S, Bischof H, Birchbauer J. Modelling fingerprint ridge orientation using Legendre polynomials. Pattern Recognition. 2010;43(1):342-357. doi:10.1016/j.patcog.2009.04.023

Cappelli R, Maio D, Maltoni D. Synthetic fingerprint-database generation. In: Object Recognition Supported by User Interaction for Service Robots. IEEE Computer Society; 2002:744-747. doi:10.1109/ICPR.2002.1048096

Cappelli R, Maio D, Lumini A, et al. Fingerprint image reconstruction from standard templates. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007;29(9):1489-1503. doi:10.1109/TPAMI.2007.1087

Zhao Q, Jain AK, Paulter NG et al. Fingerprint image synthesis based on statistical feature models. In: 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS). 2012:23-30. doi:10.1109/BTAS.2012.6374554

Cappelli R, Maio D, Maltoni D. Sfinge (synthetic fingerprint generator). CRIS Current Research Information System. Published online 2004.

Chen S, Chang S, Huang Q, et al. SVM-based synthetic fingerprint discrimination algorithm and quantitative optimization strategy. PLoS One. 2014;9(10):e111099. doi:10.1371/journal.pone.0111099

Gottschlich C, Huckemann S. Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints. IET Biometrics. 2014;3(4):291-301. doi:10.1049/iet-bmt.2013.0065

Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks. Communications of ACM. 2020;63(11):139-144. doi:10.1145/3422622

Makrushin A, Mannam VS, Dittmann J. Privacy-friendly datasets of synthetic fingerprints for evaluation of biometric algorithms. Applied Sciences. 2023;13(18):10000. doi:10.3390/app131810000

Bouzaglo R, Keller Y. Synthesis and reconstruction of fingerprints using generative adversarial networks. arXiv preprint. Published online 2022.

Fahim MA, Jung HY. A lightweight GAN network for large scale fingerprint generation. IEEE Access. 2020;8:92918-92928.

Cao K, Jain A. Fingerprint synthesis: Evaluating fingerprint search at scale. In: 2018 International Conference on Biometrics (ICB). IEEE; 2018:31-38. doi:10.1109/ICB2018.2018.00016

Riazi M, Chavoshian S, Koushanfar F. Synfi: Automatic synthetic fingerprint generation. arXiv preprint. Published online 2020.

Hildebrandt M, Dittmann J. StirTraceV3.0 and printed fingerprint detection: Simulation of acquisition condition tilting and its impact to latent fingerprint detection feature spaces for crime scene forgeries. In: 2016 4th International Conference on Biometrics and Forensics (IWBF). IEEE; 2016:1-6. doi:10.1109/IWBF.2016.7449695

Merkel R, Hildebrandt M, Dittmann J. Application of stirtrace benchmarking for the evaluation of latent fingerprint age estimation robustness. In: 3rd International Workshop on Biometrics and Forensics (IWBF 2015). IEEE; 2015:1-6. doi:10.1109/IWBF.2015.7110221

Seidlitz S, Jürgens K, Makrushin A et al. Generation of privacy-friendly datasets of latent fingerprint ımages using generative adversarial networks. In: Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications; 2021:345-352. doi:10.5220/0010251603450352

Tabassi E, Olsen M, Bausinger O, et al. NFIQ 2 NIST Fingerprint Image Quality. 2021. doi:10.6028/NIST.IR.8382

Orsini F, Cioffi A, Cipolloni L, et al. The application of artificial intelligence in forensic pathology: a systematic literature review. Front Med (Lausanne). 2025;12. doi:10.3389/fmed.2025.1583743

Campbell J. Demographic bias in biometric systems: Current research and applicable standards. Contract Report. Published online 2017:1-25.

Ross A, Jain A. Biometric sensor ınteroperability: A case study in fingerprints. In: Maltoni, D., Jain, A.K. (eds) Biometric Authentication. BioAW 2004. Lecture Notes in Computer Science, vol 3087. 2004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25976-3_13

Grother P, Tabassi E. Performance of Biometric Quality Measures. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007;29(4):531-543. doi:10.1109/TPAMI.2007.1019

Marasco E. Biases in fingerprint recognition systems: Where are we at? In: 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE; 2019:1-5. doi:10.1109/BTAS46853.2019.9186012

Badawi AM, Mahfouz M, Tadross R, et al. Fingerprint-based gender classification. IPCV. 2006;6(8).

Kralik M, Novotny V. Epidermal ridge breadth: an indicator of age and sex in paleodermatoglyphics. Variability and evolution. Published online 2003:5-30.

Hefetz I. Integrating AI systems in criminal justice: The forensic expert as a corridor between algorithms and courtroom evidence. Forensic Sciences. 2025;5(4):53. doi:10.3390/forensicsci5040053

Smith, H. K. Explainable Artificial Intelligence Approaches for Fingerprint Enhancement and Colorized Data Classification. 2025.https://www.researchgate.net/profile/Hussein-Smith/publication/398373820_Explainable_Artificial_Intelligence_Approaches_for_Fingerprint_Enhancement_and_Colorized_Data_Classification/links/6932d45a0c98040d481b3af4/Explainable-Artificial-Intelligence-Approaches-for-Fingerprint-Enhancement-and-Colorized-Data-Classification.pdf. Accesssed:17.02.2026

Horsman G. Decision support for first responders and digital device prioritisation. Forensic Science International: Digital Investigation. 2021;38:301219. doi:10.1016/j.fsidi.2021.301219

Johns Hopkins University. The Future of Forensics: How AI Can Transform Investigations. Available from: https://washingtondc.jhu.edu/news/ai-in-forensics/. (Accessed: 3th January 2026).

Farber S. AI as a decision support tool in forensic image analysis: A pilot study on integrating large language models into crime scene investigation workflows. Journal of Forensic Science. 2025;70(3):932-943. doi:10.1111/1556-4029.70035

Herke C. Automated fingerprint ıdentification: the role of artificial ıntelligence in crime scene ınvestigation. Forensic Sciences. 2026;6(1):6. doi:10.3390/forensicsci6010006

Morić Z, Dakić V, Urošev S. An AI-based decision support system utilizing bayesian networks for judicial decision-making. Systems. 2025;13(2):131. doi:10.3390/systems13020131

Montgomery RM. Augmenting forensic science through AI: The next leap in multidisciplinary approaches. Preprints (Basel). 27 January 2025. doi:10.20944/preprints202501.1951.v1

Maltoni D, Mario D, Jain AK, et al. Fingerprint classification and ındexing. In: Maltoni D, Mario D, Jain A, Prabhakar S (eds). Handbook of fingerprint recognition. 2nd ed. London:Springer. 2009:1.

Yager N, Amin A. Fingerprint classification: a review. Pattern Analytical Application. 2004;7:77-93.

Hutchins L. Systems of friction ridge classification. In: Holder EH Jr, Robinson L, Laub J, eds. The fingerprint sourcebook. National Institute of Justice; 2011:95-120.

Rodriguez J. South Atlantic crossings: Fingerprints, science, and the state in turn-of-the-century Argentina, The American historical review. American Historical Review. 2004;109(2):387.

Daluz HM. Fingerprint patterns and classification. In: Daluz HM, (ed). Fundamentals of Fingerprint Analysis. Boca Raton:FL:CRC Press; 2015b:43-58.

Koç F, Gül Y, Yavaş B, et al. Tek parmak izi tasnif sistemi. In: Tasnif Sistemleri Temel Eğitim Kitabı. 2005:21-78.

Bridges BC. Practical fingerprinting. NY:Funk and Wagnalls; 1963.

Hawthorne M. Classification Henry with FBI extension, NCIC, and IAFIS. In: Fingerprint Analysis and Understanding. Boca Raton:CRC Press; 2009:55-70.

Sodhi GS, Kaur J. The forgotten Indian pioneers of fingerprint science. Current Science. 2005;88(1):185-191.

Göl A. Parmak izinin yapısı. In: On parmak izi tasnif sistemi. Ankara: Kriminal Polis Laboratuvarları Dairesi Başkanlığı Yayını:7-20.

KPL Daire Başkanlığı. Parmak izlerinin sınıflandırılması. In: Sevinç A, ed. Temel parmak izi: kursiyer el kitabı. 1st ed. Ankara:International Police Training Center; 2011:33-43.

Demirci, S., Aydın, M., Koç, F., Gül, Y., Yavaş, B., Güneş, G., Demir, B., Tepecik, S., Ozcan, B., & Dutakü, M. (2010). Parmak izi temel eğitim kitabı (C. 17). KPL Daire Başkanlığı Yayını.

Olsen RD. Scott’s fingerprint mechanics. Thomas; 1978.

Federal Bureau of Justice. (1973). The science of fingerprints classification and uses. Içinde United States Department of Justice Federal Bureau of Investigation. 1973:87-97.

Wilson C, Candela G, Watson C. Neural network fingerprint classification. Journal of Artificial Neural Networks. 1993;1(2):203-228.

Daluz HM. Fingerprint comparisons. In: Daluz HM, (ed). Fundamentals of Fingerprint Analysis. Boca Raton:CRC Press; 2015a:237-248.

Sankaran A, Vatsa M, Singh R. Latent fingerprint matching: A survey. IEEE Access. 2014;2:982-1004. doi:10.1109/ACCESS.2014.2349879

Langenberg G. A performance study of the ACE-V process: a pilot study to measure the accuracy, precision, reproducibility, repeatability, and biasability of conclusions resulting from the ACE-V process. Journal of Forensic Identification. 2009;59(2):219.

Chen F, Zhou J. On the influence of fingerprint area in partial fingerprint recognition. In: Zheng WS, Sun Z, Wang Y, et al. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_13

Malathi S, Meena C. An efficient method for partial fingerprint recognition based on local binary pattern. In: 2010 International Conference on Communication Control and Computing Technologies. IEEE; 2010:569-572. doi:10.1109/ICCCCT.2010.5670775

Agrawal P, Kapoor R, Agrawal S. Partial fingerprint matching: Fusion of level 2 and level 3 features. In: 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence). IEEE; 2014:504-508. doi:10.1109/CONFLUENCE.2014.6949368

Siegel S. Dactylography, QUIP. Journal of Forensic Identification. 2014;64(4):428-429.

Scott B. Fingerprint Classification and Interpretation Simplified. 4th ed. Pearson; 2013.

Abbood A, Sulong G. Fingerprint classification techniques: A review. International Journal of Computer Science Issues (IJCSI). 2014;11(1).

Maltoni D. A tutorial on fingerprint recognition. Advanced Studies in Biometrics. 2005:43-68.

Darlow LN, Rosman B. Fingerprint minutiae extraction using deep learning. In: 2017 IEEE International Joint Conference on Biometrics (IJCB). IEEE; 2017:22-30. doi:10.1109/BTAS.2017.8272678

Liu W, Zhou L, Chen J. Face recognition based on lightweight convolutional neural networks. Information. 2021;12(5):191. doi:10.3390/info12050191

Zhu Y, Yin X, Jia X, et al. Latent fingerprint segmentation based on convolutional neural networks. In: 2017 IEEE Workshop on Information Forensics and Security (WIFS). IEEE; 2017:1-6. doi:10.1109/WIFS.2017.8267655

Stojanovic B, Marques O, Neskovic A, et al. Fingerprint ROI segmentation based on deep learning. In: 2016 24th Telecommunications Forum (TELFOR). IEEE; 2016:1-4. doi:10.1109/TELFOR.2016.7818799

Joshi I, Utkarsh A, Kothari R, et al. Sensor-invariant fingerprint roı segmentation using recurrent adversarial learning. In: 2021 International Joint Conference on Neural Networks (IJCNN). IEEE; 2021:1-8. doi:10.1109/IJCNN52387.2021.9533712

Ezeobiejesi J, Bhanu B. Latent fingerprint image segmentation using deep neural network. In: Bhanu B, Kumar A, (eds). Deep learning for biometrics. Cham: Springer International Publishing; 2017. p. 83–107. doi:10.1007/978-3-319-61657-5_4

Liu C, Zhi Z, Zhao W, et al. Research on local fingerprint ımage differential privacy protection method based on clustering algorithm and regression algorithm segmentation ımage. IEEE Access.2024;12:27127-27146. doi:10.1109/ACCESS.2024.3363494

Dai X, Liang J, Zhao Q, et al. Fingerprint Segmentation via Convolutional Neural Networks. In: Chinese Conference on Biometric Recognition. 2017:324-333. doi:10.1007/978-3-319-69923-3_35

Tandon S, Namboodiri A. Transformer based fingerprint feature extraction. In: 2022 26th International Conference on Pattern Recognition (ICPR). IEEE; 2022:870-876. doi:10.1109/ICPR56361.2022.9956435

Cao K, Nguyen DL, Tymoszek C, et al. End-to-end latent fingerprint search. IEEE Transactions on Information Forensics and Security. 2020;15:880-894. doi:10.1109/TIFS.2019.2930487

Engelsma JJ, Cao K, Jain AK. Learning a fixed-length fingerprint representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021;43(6):1981-1997. doi:10.1109/TPAMI.2019.2961349

Ametefe DS, Sarnin SS, Ali DM, et al. Fingerprint pattern classification using deep transfer learning and data augmentation. The Visual Computer. 7 April 2022. doi:10.1007/s00371-022-02437-x

Billah M. Explainable AI for digital forensics: ensuring transparency in legal evidence analysis. Journal of Forensic Science and Research. 3 July 2025:109-116. doi:10.29328/journal.jfsr.1001089

Hall SW, Sakzad A, Minagar S. A proof of concept ımplementation of explainable artificial ıntelligence (XAI) in digital forensics. In: Network and System Security: 16th International Conference. Denarau Island, Fiji, 9-12 December 2022:66-85. doi:10.1007/978-3-031-23020-2_4

Muaz M, Sajid S, Schulze T, et al. Explainable AI for correct root cause analysis of product quality in injection moulding. Journal of Manufacturing Processes. 2025;145:371-380.

Makrushin A, Kauba C, Kirchgasser S, et al. General requirements on synthetic fingerprint images for biometric authentication and forensic investigations. In: ACM Workshop on Information Hiding and Multimedia Security. 2021:93-104.

Kücken M. Models for fingerprint pattern formation. Forensic Science International. 2007;171(2-3):85-96. doi:10.1016/j.forsciint.2007.02.025

Ram S, Bischof H, Birchbauer J. Modelling fingerprint ridge orientation using Legendre polynomials. Pattern Recognition. 2010;43(1):342-357. doi:10.1016/j.patcog.2009.04.023

Cappelli R, Maio D, Maltoni D. Synthetic fingerprint-database generation. In: Object Recognition Supported by User Interaction for Service Robots. IEEE Computer Society; 2002:744-747. doi:10.1109/ICPR.2002.1048096

Cappelli R, Maio D, Lumini A, et al. Fingerprint image reconstruction from standard templates. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007;29(9):1489-1503. doi:10.1109/TPAMI.2007.1087

Zhao Q, Jain AK, Paulter NG et al. Fingerprint image synthesis based on statistical feature models. In: 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS). 2012:23-30. doi:10.1109/BTAS.2012.6374554

Cappelli R, Maio D, Maltoni D. Sfinge (synthetic fingerprint generator). CRIS Current Research Information System. Published online 2004.

Chen S, Chang S, Huang Q, et al. SVM-based synthetic fingerprint discrimination algorithm and quantitative optimization strategy. PLoS One. 2014;9(10):e111099. doi:10.1371/journal.pone.0111099

Gottschlich C, Huckemann S. Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints. IET Biometrics. 2014;3(4):291-301. doi:10.1049/iet-bmt.2013.0065

Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks. Communications of ACM. 2020;63(11):139-144. doi:10.1145/3422622

Makrushin A, Mannam VS, Dittmann J. Privacy-friendly datasets of synthetic fingerprints for evaluation of biometric algorithms. Applied Sciences. 2023;13(18):10000. doi:10.3390/app131810000

Bouzaglo R, Keller Y. Synthesis and reconstruction of fingerprints using generative adversarial networks. arXiv preprint. Published online 2022.

Fahim MA, Jung HY. A lightweight GAN network for large scale fingerprint generation. IEEE Access. 2020;8:92918-92928.

Cao K, Jain A. Fingerprint synthesis: Evaluating fingerprint search at scale. In: 2018 International Conference on Biometrics (ICB). IEEE; 2018:31-38. doi:10.1109/ICB2018.2018.00016

Riazi M, Chavoshian S, Koushanfar F. Synfi: Automatic synthetic fingerprint generation. arXiv preprint. Published online 2020.

Hildebrandt M, Dittmann J. StirTraceV3.0 and printed fingerprint detection: Simulation of acquisition condition tilting and its impact to latent fingerprint detection feature spaces for crime scene forgeries. In: 2016 4th International Conference on Biometrics and Forensics (IWBF). IEEE; 2016:1-6. doi:10.1109/IWBF.2016.7449695

Merkel R, Hildebrandt M, Dittmann J. Application of stirtrace benchmarking for the evaluation of latent fingerprint age estimation robustness. In: 3rd International Workshop on Biometrics and Forensics (IWBF 2015). IEEE; 2015:1-6. doi:10.1109/IWBF.2015.7110221

Seidlitz S, Jürgens K, Makrushin A et al. Generation of privacy-friendly datasets of latent fingerprint ımages using generative adversarial networks. In: Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications; 2021:345-352. doi:10.5220/0010251603450352

Tabassi E, Olsen M, Bausinger O, et al. NFIQ 2 NIST Fingerprint Image Quality. 2021. doi:10.6028/NIST.IR.8382

Orsini F, Cioffi A, Cipolloni L, et al. The application of artificial intelligence in forensic pathology: a systematic literature review. Front Med (Lausanne). 2025;12. doi:10.3389/fmed.2025.1583743

Campbell J. Demographic bias in biometric systems: Current research and applicable standards. Contract Report. Published online 2017:1-25.

Ross A, Jain A. Biometric sensor ınteroperability: A case study in fingerprints. In: Maltoni, D., Jain, A.K. (eds) Biometric Authentication. BioAW 2004. Lecture Notes in Computer Science, vol 3087. 2004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25976-3_13

Grother P, Tabassi E. Performance of Biometric Quality Measures. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007;29(4):531-543. doi:10.1109/TPAMI.2007.1019

Marasco E. Biases in fingerprint recognition systems: Where are we at? In: 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE; 2019:1-5. doi:10.1109/BTAS46853.2019.9186012

Badawi AM, Mahfouz M, Tadross R, et al. Fingerprint-based gender classification. IPCV. 2006;6(8).

Kralik M, Novotny V. Epidermal ridge breadth: an indicator of age and sex in paleodermatoglyphics. Variability and evolution. Published online 2003:5-30.

Hefetz I. Integrating AI systems in criminal justice: The forensic expert as a corridor between algorithms and courtroom evidence. Forensic Sciences. 2025;5(4):53. doi:10.3390/forensicsci5040053

Smith, H. K. Explainable Artificial Intelligence Approaches for Fingerprint Enhancement and Colorized Data Classification. 2025.https://www.researchgate.net/profile/Hussein-Smith/publication/398373820_Explainable_Artificial_Intelligence_Approaches_for_Fingerprint_Enhancement_and_Colorized_Data_Classification/links/6932d45a0c98040d481b3af4/Explainable-Artificial-Intelligence-Approaches-for-Fingerprint-Enhancement-and-Colorized-Data-Classification.pdf. Accesssed:17.02.2026

Horsman G. Decision support for first responders and digital device prioritisation. Forensic Science International: Digital Investigation. 2021;38:301219. doi:10.1016/j.fsidi.2021.301219

Johns Hopkins University. The Future of Forensics: How AI Can Transform Investigations. Available from: https://washingtondc.jhu.edu/news/ai-in-forensics/. (Accessed: 3th January 2026).

Farber S. AI as a decision support tool in forensic image analysis: A pilot study on integrating large language models into crime scene investigation workflows. Journal of Forensic Science. 2025;70(3):932-943. doi:10.1111/1556-4029.70035

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