Authors : Phebe Tryphena I.; Dr. Priyadharshi Pradhan; Dr. G. Jayanthi; S. Raghuraghavendra

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/3tuuzxt6

DOI : https://doi.org/10.38124/ijisrt/26Oct1428

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The evolution of fingerprint analysis traces its roots from the early studies by Marcello Malpighi in the 17th century to its crucial role in modern forensic science. Over time, significant advancements in technology, particularly in image processing and machine learning, have revolutionized fingerprint analysis. This paper explores the historical development of fingerprint analysis, the importance of digital processing, and the role of Automated Fingerprint Recognition Systems (AFRS). It further delves into the significance of noise removal and image-denoising techniques, highlighting the impact of state-of-the-art methods such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). The integration of deep learning techniques has significantly enhanced fingerprint-matching accuracy, speed, and reliability, marking a pivotal shift in forensic investigations and biometric authentication.

Keywords : Fingerprint Analysis, Digital Processing, Automated Fingerprint Recognition System (AFRS), Noise Removal, Image Denoising, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Deep Learning, Biometric Authentication, Forensic Science.

References :

1. Newton DE. DNA evidence and forensic science. Infobase Publishing;2008
2. Henry Faulds: the Invention of a Fingerprinter. Tredoux, Gavan. s.l. : https://galton.org/fingerprints/ faulds.htm.
3. Lawrence, Cera R., “Francis Galton (1822-1911)”. Embryo Project Encyclopaedia (2011-04-06). ISSN: 1940-5030 https:// hdl.handle.net/10776/1800
4. Moses, K.R., Higgins, P., McCabe, M., Prabhakar, S. and Swann, S., n.d. Automated Fingerprint. [online] Available at: https:// www.ojp.gov/pgffiles1/nij/225326.pdf
5. Saponara, Sergio & Elhanashi, Abdussalam & Zheng, Qinghe. (2021). Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture. IEEE Access. PP. 1-1. 10.1109/ACCESS.2021.3124746.
6. Daluz HM. Fundamentals of fingerprint analysis. CRC Press; 2018 Oct 26.
7. Brislawn CM. Fingerprints go digital. Notices of the AMS. 1995 Nov;42(11):1278-83.
8. Sharma, T., Chaitan, S.M., Somayaji, N.S., Mahajan, B., Rajguru, J. P., Hajibabaei, S., & Hegde, S. (2020). The medicolegal importance of establishing human identity by using dactyloscopy and rugoscopy: A comparative study. Journal of family medicine and primary care, 9(7), 3236-3241. Https://doi.org/10.4103/jfmpc.jfmpc_545_20.
9. Ferreia, Rafaela & Paula, Rachel & Okuma, Adriana & Costa, Luciana. (2021). Fingerprint Development Techniques: A Review. Revista Virtual de Química. 13. 10.21577/1984-6835.20210083
10. Moses, K.R. & Higgins, P. & Mccabe, M. & Prabhakar, S. & Swann, S..(2015). Automated Fingerprint Identification System (AFIS).
11. An intelligent system for automatic fingerprint identification using feature fusion by Gabor filter and deep learning. Nur-A-Alam a, M. Ahsan b, M.A. Based c, J. Haider d, M. Kowalski. s.l. :https;// doi.org/10.1016/j.compeleceng.2021.107387, October 2021, Vol. Volume 95. 107387.
12. A new technique for visualization of latent fingerprints on various surfaces using powder from turmeric: A rhizomatous herbaceous plant (Curcuma longa). Rakesh K. Garg, Harish Kumari, j.ejfs.2011.04.011, 2021, Vol. 1. ISSN 2090-536X.
13. Jain, Anil & Feng, Jianjiang. (2011). Latent Fingerprint Matching. IEEE transactions on pattern analysis and machine intelligence. 33. 88-100. 10.1109/TPAMI.2010.59.
14. Zanganesh, Omid & Bala, Srinivasan & Bhattacharjee, Nandita. (2015). Partial Fingerprint Matching through Region-Based Similarity. 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014. 10.1109/DICTA.2014.7008121.
15. About your Aadhaar [Internet]. Unique Identification Authority of India Government of India. Available from: https://uidai.gov.in/en/myaadhaar/about-your-aadhaar.html.
16. Barnes, C., & Conten, J. (2021, June). Chapter history (NCJ No. 225321). National Institute of Justice. https://www.ojp.gov/pdffiles1/nij/225321.pdf
17. Biometric Devices [Internet]. Unique Identification Authority of India | Government of India. Available from: https://uidai.gov.in/en/ ecosystem/authentication-devices-documents/ biometric-devices.html.
18. Operation Model [Internet]. Unique Identification Authority of India | Government of India. Available from: https://uidai.gov.in/en/ ecosystem/authentication-ecosystem/operationmodel.html.
19. Security in UIDAI system [Internet]. Unique Identification Authority of India | Government of India. Available from: https://uidai.gov.in/en/myaadhaar/about-your-aadhaar/security-in-uidaisystem.html.
20. Dixon P. (2017). A failure to “Do No Harm” - - India's Aadhaar biometric ID program and its inability to protect privacy in relation to measures in Europe and the U.S. Health and Technology, 7(4), 539-567. http://doi.org/10.1007/s12553-107-0202-6.
21. Devi, Trishna. "Biometric Data, Identification and Authentication in India - Legal Framework, Challenges and Impact." International Journal of Law Management & Humanities, vol. 4, no. 3, 2021, pp. 1001-1036. DOI:10.10000/IJLMH.11561. https:// doij.org/
22. Sarfraz N. (2019). Adermatoglyphia: Barriers to Biometric Identification and the Need for a Standardized Alternative. Cureus, 11(2), e4040. https://doi.org/10.7759/cures.4040.
23. EGIPMR, 2016. The Gazette of India Extraordinary [Part II] [online] Available at: [Accessed 16 October 2024].
24. LARRDIS, Lok Sabha Secretariat, n.d. Right to Privacy: Members’ Reference Service. New Delhi: Lok Sabha Secretariat. Available at: https:// loksabhadocs.nic.in/Refinput/ New_Reference_Notes/English/ Right%20to%20Priva
25. Government of India. Ministry of Electronics and Information Technology, Government of India | Home Page [Internet]. www.meity.gov.in.2023. Available from https://www.meity.gov.in/ writeraddata/files/  Digital%20Personal%20Data%20Protection%20Act %202023.p.
26. Hamid, Ainul & Rahim, Mohd & Almazyad, Abdulaziz & Saba, Tanzila. (2015). Analysis of Proposed Noise Detection & Removal Technique in Degraded Fingerprint Images. 3D Research. 6.10.1007/s13319-015-0067-2.
27. Wu, Chaohong & Shi, Zhixin & Govindaraju, Venu. (2004). Fingerprint image enhancement method using directional median filter. Proceedings of SPIE - The International Society for Optical Engineering. 10.1117/12.542200.
28. Xie, Shan & Yang, Jucheng & Park, Dong & Yoon, Sook & Jinwook, Shin. (2011). Fingerprint Quality Analysis and Estimation Approach for Fingerprint Matching. 10.5772/20474.
29. Yadav G. Yadav DK, Mouli PC. Statistical measures for Palmprint Image enhancement. InMachine Learning for Biometrics 2022 Jan 1 (pp. 65-85). Academic Press
30. Shreya S, Chatterjee K. Gan-enable latent fingerprint enhancement model for human identification system. Multimedia Tools and Applications. 2024 Mar;83(9):27565-88
31. Wang H, Ke R, Jiang X. Large-region image inpainting using adaptive Fourier Neural Network and space-frequency domain structural analysis. Journal of Intelligent & Fuzzy Systems. (Preprint):1-5.
32. Yang, Jainwei & Liu, Lifeng & Jaing, Tianzi & Fan, Yong. (2003). A modified Gabor filter design method for fingerprint Image enhancement. Pattern Recognition Letters. 24. 1805-1817. 10.1016/ S0167-8655(03)00005-9
33. Liu K, Li J, Hussain Bukhari SS. Overview of Image inpainting and forensic technology. Security and Communication Networks. 2022;2022(1):9291971.
34. He, Yuliang & Tian, Jie & Xiping & Zhang, Tanghui. (2003). Image enhancement and minutiae matching in fingerprint verification. Pattern Recognition Letters. 24. 1349-1360. 10.1016/ S0167-8655(02)00376-8.
35. Hong, Lin & Wan, Yifei & AK, Jain. (1998). Fingerprint Image Enhancement: Algorithm and Performance Evaluation. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 20. 777- 789. 10.1109/34.709565.
36. Kocevar, Marco & Kotnik, Bonjan & Chowdhury, Amor & Kacic, Zdravko. (2014). Realtime fingerprint image enhancement with a two-stage algorithm and block-local normalization. Journal of Real-Time Image Processing. 13.10.1007/ s11554-014-0440-z.
37. Chang, T.L., 1980. Texture Analysis of Digitized Fingerprints for Singularity Detection. NATO Conference Series, Series 4: Marine Science, 1, pp.478-480.
38. Pradeep, N. (2022). A Revolutionary Fingerprint Authentication Approach Using Gabor Filters for Feature Extraction and Deep Learning Classification Using Convolutional Neural Networks. 10.1007/978-981-16-8512-5_38.
39. Shreya, Shashi & Chatterjee, Kakali. (2023). GAN-enable latent fingerprint enhancement model for human identification system. Multimedia Tools and Applications. 1-24. 10.1007/ s11042-023-16510-7.
40. Yang, Kang & Lai, Kunhao. (2023). NaturalFinger: Generating Natural Fingerprint with Generative Adversarial Networks.
41. Agagu, Modupe. (2023). Development of an improved Multi-filtering Matching Model for Fingerprint Recognition. Journal of Image Processing and Intelligent Remote Sensing. 24-38. 10.55529/jipirs.35.24.38.
42. Jiajialei, Qinmupeng, Xingeyou, Hatemjabbar, H. and Wang, P., 2012. Fingerprint Enhancement Based on Wavelet and Anisotropic Filtering. International Journal of Pattern Recognition and Artificial Intelligence, 26. doi:10.1142/ S0218001412560010.
43. Yadov, Vandana & Bharadi, Vinayak & Yadav, Sunil. (2016). Texture Feature Extraction Using Hybrid Wavelet Type I & II for Fingerprint Knuckle Prints for Multi-algorithmic Feature Fusion. Procedia Computer Science. 79. 359-366. 10.1016/ j.procs.2016.03.047.
44. Ravikumar, Aswathy & Sriraman, Harini. (2023). Understanding Convolutional Neural Network With Tensorflow: CNN. 10.4018/978-1-6684-8531-6.ch003.
45. Chiroma, Haruna. (2021). Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review. Journal of Artificial Intelligence and Systems. 3. 157-197. 10.33969/ AIS.2021.31010.
46. Jin, Jiani. (2022). Convolutional Neural Networks for Biometrics Applications. SHS Web of Conferences. 144. 03013. 10.1051/shsconf/ 202214403013
47. Saponara, Sergio & Elhanashi, Abdussalam & Zheng, Qinghe. (2021). Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture. IEEE Access. PP. 1-1. 10.1109/ACCESS.2021.3124746.
48. Zhang, Yilong & Yu, Shichang & Pu, Shiliang & Wang, Yingyu & Wang, Kanlei & Sun, Haohao & Wang, Haixia. (2023). 3D CNN-based fingerprint anti-spoofing through optical coherence tomography. Heliyon. 9. e20052. 10.1016/j.heliyon.2023.e20052.
49. Baghel, Vivek & Patel, Smit & Prakash, Surya & Srivastava, Akhilesh. (2023). A Deep Learning Based Approach to Perform Fingerprint Matching. 10.1007/978-3-031-22018-0_22.
50. Handling Data Scarcity Through Data Augmentation in Training of Deep Neural Network for 3D Data Processing. International Journal on Semantic Web and Information System. 18. 1-16. 10.4018/IJWIS.297038. . Srivastava, Akhilesh & Rotte, Priyanka & Jain, Arushi & Prakash, Surya. (2022).