A Study on Automatic Latent Fingerprint Identification System

Authors

  • Uttam U Deshpande KLS Gogte Institute of Technology, Udyambag, Belagavi, Karnataka, India
  • V. S. Malemath KLE Dr. M.S. Sheshgiri College of Engineering and Technology,Udyambag, Belagavi,India

DOI:

https://doi.org/10.30564/jcsr.v4i1.4388

Abstract

Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification. Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts. However, since the latent fingerprints are accidentally leftover on different surfaces, the lifted prints look inferior. Therefore, a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance. As a result, there is an ever-growing demand to develop reliable and robust systems. In this regard, we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition, segmentation, quality assessment, enhancement, feature extraction, and matching steps. Later, we provide insight into different benchmark latent datasets available to perform research in this area. Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation, enhancement, extraction, and matching approaches to strengthen the research.

Keywords:

Fingerprint identification system, NIST, Latent fingerprints, Forensics fingerprint database

References

[1] Integrated pattern recognition and biometrics lab, West Virginia University.

[2] Hawthorne, M.R., 2009. Fingerprints analysis and understanding. CRC Press, Taylor and Francis Group, Boca Raton, London, New York.

[3] Jain, A.K., Flynn, P., Ross, A.A., 2007. Handbook of biometrics. Springer Science and Business Media, New York.

[4] Kawagoe, M., Tojo, A., 1984. Fingerprint pattern classification. Pattern Recognition. 17(3), 295-303.

[5] Sankaran, A., Vatsa, M., Singh, R., 1984. Latent fingerprint matching: A survey. IEEE Access. 2(2014), 982-1004.

[6] Maltoni, D., Maio, D., Jain, A.K., et al., 2009. Handbook of Fingerprint Recognition. 2nd ed. New York, NY, USA: Springer-Verlag.

[7] Evaluation of Latent Fingerprint Technologies. [Online] Available: http://www.nist.gov/itl/iad/ig/latent.cfm. (Accessed Oct. 24, 2013).

[8] Ashbaugh, D., 1999. Quantitative-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology. Boca Raton, FL, USA: CRC Press. pp. 134-135.

[9] Ulery, B.T., Hicklin, R.A., Buscaglia, J., et al., 2011. Accuracy and reliability of forensic latent fingerprint decisions. Proc. Nat. Acad. Sci. USA. 108(19), 7733- 7738.

[10] Jain, A.K., Feng, J., Jan. 2011. Latent fingerprint matching. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 88-100.

[11] Fact Sheet, 2013. Integrated Automated Fingerprint Identification System.

[12] Shrestha, H., Dhasarathan, C., Kumar M., et al., 2022. A Deep Learning Based Convolution Neural Network-DCNN Approach to Detect Brain Tumor. In: Gupta G., Wang L., Yadav A., Rana P., Wang Z. (eds) Proceedings of Academia-Industry Consortium for Data Science. Advances in Intelligent Systems and Computing, vol 1411. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-16-6887-6_11.

[13] Bhardwaj, A., Mohamed, A., Kumar, M., 2021. Real-time Privacy-Preserving Framework for COVID-19 Contact Tracing. Computers, Materials and Continua. 70. DOI: https://doi.org/10.32604/cmc.2022.018736.

[14] Singh, S., Chintalacheruvu, S.C.K., Garg, S., et al., 2021. Efficient Face Identification and Authentication Tool for Biometric Attendance System. 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN). pp. 379-383. DOI: https://doi.org/10.1109/SPIN52536.2021.9565990.

[15] Diefenderfer, G.T., 2006. Fingerprint Recognition. DTIC Document, Naval Post Graduate School, Monterey, California.

[16] Choi, H., Boaventura, M., Boaventura, I.A., et al., 2012. Automatic segmentation of latent fingerprints. In IEEE International Conference on Biometrics: Theory, Applications and Systems. pp. 303-310.

[17] Cao, K., Liu, E., Jain, A.K., 2014. Segmentation and enhancement of latent fingerprints: A coarse to fine ridge structure dictionary. in IEEE Transactions on Pattern Analysis and Machine Intelligence. 36(9), 1847-1859.

[18] Ruangsakul, P., Areekul, V., Phromsuthirak, K., et al., 2015. Latent fingerprints segmentation based on rearranged fourier subbands. in IEEE International Conference on Biometrics. pp. 371-378.

[19] Liu, S., Liu, M., Yang, Z., 2016. Latent fingerprint segmentation based on linear density. In IEEE International Conference on Biometrics. pp. 1-6.

[20] Zhu, Y., Yin, X., Jia, X., et al., 2017. Latent fingerprint segmentation based on convolutional neural networks,” in IEEE Workshop on Information Forensics and Security. pp. 1-6.

[21] Ezeobiejesi, J., Bhanu., B., 2017. Latent fingerprint image segmentation using deep neural network. in Deep Learning for Biometrics. Springer. pp. 83-107.

[22] Nguyen, D.L., Cao, K., Jain., A.K., 2018. Robust minutiae extractor: Integrating deep networks and fingerprint domain knowledge. in IEEE International Conference on Biometrics.

[23] Khan, A.I., Wani, M.A., 2019. Patch-based Segmentation of Latent Fingerprint Images Using Convolutional Neural Network. in Appl. Artif. Intell. 33(1), 87-100. DOI: https://doi.org/10.1080/08839514.2018.1526704.

[24] Fingerprint Minutiae from Latent and Matching Tenprint Images - NIST special database 27 (a); 2010.

[25] Hornak, L., Ross, A., Crihalmeanu, S.G., et al., 2007. A protocol for multibiometric data acquisition storage and dissemination. tech. rep., West Virginia University.

[26] Hicklin, A., 2007. Latent quality survey, Noblis .Org, Falls Church, VA, USA.

[27] Karimi, A.S., Kuo, C.C.J., 2008. A robust technique for latent finger- print image segmentation and enhancement. in Proceedings of the 15th IEEE International Conference on Image Processing. pp. 1492- 1495.

[28] Ezhilmaran, D., Adhiyaman, M., 2016. Edge Detection Method for Latent Fingerprint Images Using Intuitionistic Type-2 Fuzzy Entropy. Cyber- netics and Information Technologies. 16(3), 205-218.

[29] Yoon, S., Liu, E., Jain, A.K., 2012. On latent fingerprint image quality. In Proceedings of the International Workshop on Computational Forensics.

[30] Feng, J., Zhou, J., Jain, A.K., 2013. Orientation field estimation for latent fingerprint enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35(4), 925-940.

[31] Li, J., Feng, J., Kuo, C.C.J., 2018. Deep-Convolutional Neural Network for latent fingerprint enhancement. Signal Process. Image Commun. 60, 52-63.

[32] Joshi, I., Anand, A., Vatsa, M., et al., 2019. Latent Fingerprint Enhancement Using Generative Adversarial Networks. in {IEEE} Winter Conference on Applications of Computer Vision, {WACV}, Waikoloa Village, HI, USA. pp. 895-903. DOI: https://doi.org/10.1109/WACV.2019.00100.

[33] Sankaran, A., Pandey, P., Vatsa, M., 2014. On latent fingerprint minutiae extraction using stacked denoising sparse autoencoders. In Proceedings of the International Joint Conference on Biometrics. pp. 1-7.

[34] Paulino, A., Liu, E., Cao, K., et al., 2013. Latent fingerprint indexing: Fusion of Level- 1 and Level- 2 features. In Proceedings of the IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems. pp. 1-8.

[35] Su, C., Srihari, S., 2010. Latent fingerprint core point prediction based on Gaussian processes. In Proceedings of the 20th International Confer- ence on Pattern Recognition. pp. 1634-1637.

[36] Tang, Y., Gao, F., Feng, J., et al., 2017. FingerNet: An unified deep network for fingerprint minutiae extraction. in IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA. pp. 108-116. DOI: https://doi.org/10.1109/BTAS.2017.8272688.

[37] Darlow, L.N., Rosman, B., 2017. Fingerprint minutiae extraction using deep learning. in IEEE International Joint Conference on Biometrics, (IJCB), Denver, CO, USA. pp. 22-30. DOI: https://doi.org/10.1109/BTAS.2017.8272678.

[38] Deshpande, U.U., Malemath, V.S., 2021. MINU-EXTRACTNET: Automatic Latent Fingerprint Feature Extraction System Using Deep Convolutional Neural Network. In Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2020. Communications in Computer and Information Science, vol 1380. Springer, Singapore. pp. 44-56. DOI: https://doi.org/10.1007/978-981-16-0507-9_5.

[39] Liu, E., Arora, S.S., Cao, K., et al., 2013. A feedback paradigm for latent fingerprint matching. In Proceeding of the IEEE International Conference on Biometrics. pp. 1-8.

[40] Miguel Angel Medina-Pérez, Aythami Morales Moren,, Miguel Ángel Ferrer Ballester, et al., 2016. Latent fingerprint identification using deformable minutiae clustering. Elsevier, Neurocomputing. 175, 851-865.

[41] Deshpande, U.U., Malemath, V.S., Patil, S.M., et al., 2021. Latent fingerprint identification system based on local combination of minutiae feature points. Journal of SN Computer Science (SNCS), Springer,Article number - 206. DOI: https://doi.org/10.1007/s42979-021-00615-7.

[42] Cao, K., Jain, A.K., 2018. Latent Fingerprint Recognition: Role of Texture Template. in 9th IEEE International Conference on Biometrics Theory, Applications and Systems, (BTAS), Redondo Beach, CA, USA. pp. 1-9. DOI: https://doi.org/10.1109/BTAS.2018.8698555.

[43] Ezeobiejesi, J., Bhanu, B., 2018. Patch Based Latent Fingerprint Matching Using Deep Learning. 25th IEEE International Conference on Image Processing (ICIP), Athens. pp. 2017-2021. DOI: https://doi.org/10.1109/ICIP.2018.8451567.

[44] Nguyen, D.L., Jain, A.K., 2019. End-to-End Pore Extraction and Matching in Latent Fingerprints: Going BeyondMinutiae. CoRR, vol. abs/1905.11472. [Online] Available: http://arxiv.org/abs/1905.11472.

[45] Deshpande, U.U., Malemath, V.S., Patil, S.M., et al., 2020. CNNAI: A Convolution Neural NetworkBased Latent Fingerprint Matching Using the Combination of Nearest Neighbor Arrangement Indexing. Front. Robot. AI, vol. 7. DOI: https://doi.org/10.3389/frobt.2020.00113.

[46] Feng, J., Shi, Y., Zhou, J., 2012. Robust and efficient algorithms for separating latent overlapped fingerprints. IEEE Transactions on Information Forensics and Security. 7(5), 1498-1510.

[47] Sankaran, A., Vatsa, M., Singh, R., 2015. Multisensor Optical and Latent Fingerprint Database. in IEEE Access. 3, 653-665.

Downloads

How to Cite

Deshpande, U. U., & Malemath, V. S. (2022). A Study on Automatic Latent Fingerprint Identification System. Journal of Computer Science Research, 4(1), 38–50. https://doi.org/10.30564/jcsr.v4i1.4388

Issue

Vol. 4 , Iss. 1 (January 2022)

Article Type

Article

License

Copyright © 2022 Author(s)

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC