Fingerprint liveness detection using dynamic local ternary pattern (DLTP)
DOI:
https://doi.org/10.21015/vtse.v12i2.1842Abstract
Nowadays, biometric confirmation systems are utilized for security applications such as verification and identification. There are various biometric modalities such as fingerprints, face recognition, and iris scans. Biometric systems are superior to PIN and password-based systems because the latter can be easily stolen or forgotten, whereas biometric traits are unique and difficult to replicate or forget. Among biometric modalities, fingerprint recognition is widely employed for security purposes due to the distinctiveness of each individual's fingerprint. However, fingerprint biometric systems encounter challenges, such as the reproduction of fake fingerprints using materials like silicon, which can potentially bypass security measures. This issue is considered a significant problem in fingerprint systems.This paper proposes a new software-based method called Dynamic Local Ternary Pattern (DLTP) for fingerprint liveness detection, employing a machine learning classifier, specifically Support Vector Machine (SVM), to distinguish between live and fake fingerprints. Various experiments were conducted using DLTP and state-of-the-art texture descriptors. The results obtained from DLTP demonstrated optimal accuracy, clearly surpassing those achieved by the Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) texture descriptors reported in previous studies.
References
M. Kumar and P. Singh, "Liveness detection and recognition system for fingerprint images," in *Innovations in Electronics and Communication Engineering: Proceedings of the 8th ICIECE 2019*, Springer Singapore, 2020, pp. 467-477.
L. S. Mohan and J. James, "Fingerprint spoofing detection using HOG and local binary pattern," *International Journal of Advanced Research in Computer and Communication Engineering*, vol. 6, no. 4, pp. 586-593, 2017.
R. P. Sharma, A. Anshul, A. Jha, and S. Dey, "Investigating fingerprint quality features for liveness detection," in *Mining Intelligence and Knowledge Exploration: 7th International Conference, MIKE 2019, Goa, India, December 19–22, 2019, Proceedings 7*, Springer International Publishing, 2020, pp. 296-307.
D. S. Ametefe, S. S. Sarnin, D. M. Ali, and M. Z. Zaheer, "Fingerprint liveness detection schemes: A review on presentation attack," *Computer Methods in Biomechanics and Biomedical Engineering: Imaging Visualization*, vol. 10, no. 2, pp. 217-240, 2022.
J. Fei, Z. Xia, P. Yu, and F. Xiao, "Adversarial attacks on fingerprint liveness detection," *EURASIP Journal on Image and Video Processing*, vol. 2020, no. 1, p. 1, 2020.
M. Kumar and P. Singh, "Liveness detection and recognition system for fingerprint images," in *Innovations in Electronics and Communication Engineering: Proceedings of the 8th ICIECE 2019*, Springer Singapore, 2020, pp. 467-477.
F. Demenschonok, J. Harrigan, and T. Bonaci, "An Overview of Fingerprint-Based Authentication: Liveness Detection and Beyond," *arXiv preprint arXiv:2001.09183*, 2020.
A. S. Ahmad, R. Hassan, and R. M. Othman, "An investigation of fake fingerprint detection approaches," in *AIP Conference Proceedings*, vol. 1891, no. 1, p. 020020, AIP Publishing, 2017.
S. Arora and M. P. S. Bhatia, "Challenges and opportunities in biometric security: A survey," *Information Security Journal: A Global Perspective*, vol. 31, no. 1, pp. 28-48, 2022.
Y. Jiang and X. Liu, "Uniform local binary pattern for fingerprint liveness detection in the Gaussian pyramid," *Journal of Electrical and Computer Engineering*, vol. 2018, no. 1, p. 1539298, 2018.
H. Sathyaveti and A. Jadda, "Fingerprint Liveness Detection from Single Image Using SURF PHOG," 2015.
R. K. Dubey, J. Goh, and V. L. Thing, "Fingerprint liveness detection from single image using low-level features and shape analysis," *IEEE Transactions on Information Forensics and Security*, vol. 11, no. 7, pp. 1461-1475, 2016
R. F. Nogueira, R. de Alencar Lotufo, and R. C. Machado, "Fingerprint liveness detection using convolutional neural networks," *IEEE Transactions on Information Forensics and Security*, vol. 11, no. 6, pp. 1206-1213, 2016.
C. Yuan, X. Li, Q. J. Wu, J. Li, and X. Sun, "Fingerprint liveness detection from different fingerprint materials using convolutional neural network and principal component analysis," *Computers, Materials & Continua*, vol. 53, no. 3, pp. 357-371, 2017.
W. Kim, "Fingerprint liveness detection using local coherence patterns," *IEEE Signal Processing Letters*, vol. 24, no. 1, pp. 51-55, 2017.
L. Ghiani, D. A. Yambay, V. Mura, G. L. Marcialis, F. Roli, and S. A. Schuckers, "Review of the Fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015," *Image and Vision Computing*, vol. 58, pp. 110-128, 2017.
D. Gragnaniello, G. Poggi, C. Sansone, and L. Verdoliva, "Wavelet-Markov local descriptor for detecting fake fingerprints," *Electronics Letters*, vol. 50, no. 6, pp. 439-441, 2014.
L. Ghiani et al., "LivDet 2013 fingerprint liveness detection competition 2013," in *2013 International Conference on Biometrics (ICB)*, pp. 1-6, IEEE, 2013.
J. M. Singh, A. Madhun, G. Li, and R. Ramachandra, "A survey on unknown presentation attack detection for fingerprint," in *Intelligent Technologies and Applications: Third International Conference, INTAP 2020, Grimstad, Norway, September 28–30, 2020, Revised Selected Papers 3*, Springer International Publishing, 2021, pp. 189-202.
Y. Zhang et al., "A score-level fusion of fingerprint matching with fingerprint liveness detection," *IEEE Access*, vol. 8, pp. 183391-183400, 2020.
L. Chen et al., "A Finger Vein Liveness Detection System Based on Multi-Scale Spatial-Temporal Map and Light-ViT Model," *Sensors*, vol. 23, no. 24, p. 9637, 2023.
A. Rai, S. Dey, P. Patidar, and P. Rai, "Mosfpad: an end-to-end ensemble of mobilenet and support vector classifier for fingerprint presentation attack detection," *arXiv preprint arXiv:2303.01465*, 2023.
R. P. Sharma and S. Dey, "A comparative study of handcrafted local texture descriptors for fingerprint liveness detection under real world scenarios," *Multimedia Tools and Applications*, vol. 80, no. 7, pp. 9993-10012, 2021.
S. Parveen et al., "Face liveness detection using dynamic local ternary pattern (DLTP)," *Computers*, vol. 5, no. 2, p. 10, 2016.
S. Galbally, J. Marcel, and J. Fierrez, "Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint and Face Recognition," *IEEE Transactions on Image Processing*, vol. 23, no. 2, pp. 710-724, 2014.
V. Mura et al., "LivDet 2017 fingerprint liveness detection competition 2017," in *2018 International Conference on Biometrics (ICB)*, pp. 297-302, IEEE, 2018.
L. Ghiani et al., "Review of the fingerprint liveness detection (LivDet) competition series: 2009 to 2015," *Image and Vision Computing*, vol. 58, pp. 110-128, 2017.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution License CC BY
