Articles Open Access
Deep Learning-Based Contactless Biometric Authentication Systems for Secure Digital Identity Verification
Responsable Technique, Groupe Vicat, Clermont-Ferrand, France
Abstract
The rapid expansion of digital services has increased the need for secure, reliable, and user-friendly authentication mechanisms. Traditional authentication methods such as passwords, PINs, and token-based systems are increasingly vulnerable to cyberattacks, identity theft, and phishing attempts. As a result, biometric authentication systems have gained significant attention for enhancing digital identity verification. In particular, contactless biometric authentication powered by deep learning techniques has emerged as a promising solution for secure and seamless user authentication. This study explores deep learning-based contactless biometric authentication systems, focusing on modalities such as face recognition, iris recognition, voice recognition, gait analysis, and palm-vein recognition. These systems leverage convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures to extract and analyze complex biometric features with high accuracy. The research examines system architecture, feature extraction techniques, model training processes, and real-time deployment challenges in digital identity verification systems. Additionally, the study investigates security vulnerabilities such as spoofing attacks, adversarial machine learning threats, and privacy concerns associated with biometric data. Findings suggest that deep learning significantly improves authentication accuracy, robustness, and scalability while enabling real-time contactless verification in diverse environments. However, challenges related to data privacy, computational complexity, bias in training datasets, and system interpretability remain critical issues. The study concludes that deep learning-based contactless biometric authentication systems play a vital role in strengthening secure digital identity verification frameworks for modern digital ecosystems.
Keywords
DOI
PDF Download
Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 4 |
| Number | 4 |
| Pages | 6491-6497 |
| Published | 2020-07-04 |
| Issue | Vol. 4 No. 4 (2020): International Research Journal of Innovative Engineering (IRJIE) |
References
1. Deng, J., Guo, J., Niannan, X., & Zafeiriou, S. (2019). ArcFace: Additive angular margin loss for deep face recognition. Proceedings of CVPR, 4690–4699.
2. Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). DeepFace: Closing the gap to human-level performance in face verification. CVPR, 1701–1708.
3. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition. CVPR, 815–823.
4. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. CVPR, 770–778.
5. Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. NeurIPS, 1097–1105.
6. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. ICLR.
7. Masi, I., Tran, A. T., Hassner, T., Leksut, J., & Medioni, G. (2018). Do we really need to collect millions of faces for effective face recognition? ECCV, 579–596.
8. Ranjan, R., Patel, V. M., & Chellappa, R. (2017). HyperFace: A deep multi-task learning framework. IEEE Transactions on Pattern Analysis and Machine Intelligence.
9. Wang, H., & Deng, W. (2018). Deep face recognition: A survey. Neurocomputing, 335, 1–20.
10. Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2017). Joint face detection and alignment using multitask cascaded CNNs. IEEE Signal Processing Letters.
11. Ouyang, W., Wang, X., Zhang, C., Yang, X., & Wang, Q. (2016). Factors in deep face recognition. ICCV.
12. Nogueira, R. F., & Vural, E. (2016). Fingerprint liveness detection using convolutional networks. IEEE Transactions on Information Forensics and Security.
13. Marcel, S., Nixon, M. S., & Li, S. Z. (Eds.). (2016). Handbook of biometric anti-spoofing. Springer.
14. Li, S. Z., & Jain, A. K. (2015). Encyclopedia of biometrics. Springer.
15. Ross, A., Nandakumar, K., & Jain, A. K. (2016). Handbook of multibiometrics. Springer.
16. Galbally, J., Marcel, S., & Fierrez, J. (2014). Biometric anti-spoofing methods. IEEE Signal Processing Magazine.
17. Kumar, A., & Zhou, Y. (2018). Human identification using palm vein patterns. IEEE Transactions on Biometrics.
18. Abate, A. F., Nappi, M., Riccio, D., & Sabatino, G. (2017). 2D and 3D face recognition. Pattern Recognition Letters.
19. Goodfellow, I., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. ICLR.
20. Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017). SphereFace: Deep hypersphere embedding for face recognition. CVPR.
21. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
22. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2015). Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining. Middle-East Journal of Scientific Research, 23(3), 405-412.
23. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
24. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
25. Sugumar, R. B., & Anandharaj, K. (2016). Assessment of Bacterial Load in the Fresh Water Lake System of Tamil Nadu. Interl J Curr Microbiol App Sci, 5(6), 236-246.
26. Rengarajan, R. S. A. (2016). Secure verification technique for defending IP spoofing attacks.
27. Mathew, A. R., & Al Hajj, A. (2017). Secure communications on IoT and big data. Indian Journal of Science and Technology, 10(11).
28. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
29. Sasidevi, J., Sugumar, R., & Priya, P. S. (2015). New-Multi-Phase Distribution Network Intrusion Detection. International Journal, 5(3).
30. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
31. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
32. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
33. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
34. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
35. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
36. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
37. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338-356.
38. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
39. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
40. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
41. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
42. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
43. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
44. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
45. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
46. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
2. Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). DeepFace: Closing the gap to human-level performance in face verification. CVPR, 1701–1708.
3. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition. CVPR, 815–823.
4. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. CVPR, 770–778.
5. Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. NeurIPS, 1097–1105.
6. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. ICLR.
7. Masi, I., Tran, A. T., Hassner, T., Leksut, J., & Medioni, G. (2018). Do we really need to collect millions of faces for effective face recognition? ECCV, 579–596.
8. Ranjan, R., Patel, V. M., & Chellappa, R. (2017). HyperFace: A deep multi-task learning framework. IEEE Transactions on Pattern Analysis and Machine Intelligence.
9. Wang, H., & Deng, W. (2018). Deep face recognition: A survey. Neurocomputing, 335, 1–20.
10. Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2017). Joint face detection and alignment using multitask cascaded CNNs. IEEE Signal Processing Letters.
11. Ouyang, W., Wang, X., Zhang, C., Yang, X., & Wang, Q. (2016). Factors in deep face recognition. ICCV.
12. Nogueira, R. F., & Vural, E. (2016). Fingerprint liveness detection using convolutional networks. IEEE Transactions on Information Forensics and Security.
13. Marcel, S., Nixon, M. S., & Li, S. Z. (Eds.). (2016). Handbook of biometric anti-spoofing. Springer.
14. Li, S. Z., & Jain, A. K. (2015). Encyclopedia of biometrics. Springer.
15. Ross, A., Nandakumar, K., & Jain, A. K. (2016). Handbook of multibiometrics. Springer.
16. Galbally, J., Marcel, S., & Fierrez, J. (2014). Biometric anti-spoofing methods. IEEE Signal Processing Magazine.
17. Kumar, A., & Zhou, Y. (2018). Human identification using palm vein patterns. IEEE Transactions on Biometrics.
18. Abate, A. F., Nappi, M., Riccio, D., & Sabatino, G. (2017). 2D and 3D face recognition. Pattern Recognition Letters.
19. Goodfellow, I., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. ICLR.
20. Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017). SphereFace: Deep hypersphere embedding for face recognition. CVPR.
21. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
22. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2015). Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining. Middle-East Journal of Scientific Research, 23(3), 405-412.
23. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
24. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
25. Sugumar, R. B., & Anandharaj, K. (2016). Assessment of Bacterial Load in the Fresh Water Lake System of Tamil Nadu. Interl J Curr Microbiol App Sci, 5(6), 236-246.
26. Rengarajan, R. S. A. (2016). Secure verification technique for defending IP spoofing attacks.
27. Mathew, A. R., & Al Hajj, A. (2017). Secure communications on IoT and big data. Indian Journal of Science and Technology, 10(11).
28. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
29. Sasidevi, J., Sugumar, R., & Priya, P. S. (2015). New-Multi-Phase Distribution Network Intrusion Detection. International Journal, 5(3).
30. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
31. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
32. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
33. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
34. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
35. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
36. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
37. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338-356.
38. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
39. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
40. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
41. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
42. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
43. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
44. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
45. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
46. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
Statistics
68
Views
59
Downloads