Mivar expert system for an access control system based on palm vein biometric identification

The paper addresses the task of designing an access control system (ACS) capable of making informed decisions not only on the basis of a biometric template but also taking into account the context: employee access level, zone of access, time-of-day policy and authentication history. An architecture of a complex intelligent system combining neural-network and logical levels of artificial intelligence is proposed. Biometric identification is implemented using a ResNet18 convolutional neural network adapted for grayscale palm-vein images and trained on a dataset of 834 subjects (8,340 images) using triplet metric learning with a classification head; Top-1 accuracy of 87.47 %, Top-5 of 96.58 %, Top-10 of 98.14 %, ROC-AUC of 0.9985 and EER of 1.64 % are achieved with an average confidence of correct matches equal to 0.908. The neural-network confidence together with five contextual parameters is passed to a mivar expert system (MES) implemented in the KESMI Wi!Mi Razumator environment. The MES contains three independent relations with shared inputs that produce an access decision, an alert level and a biometric reliability estimate. Shared inputs induce cross-edges in the bipartite solution graph, reflecting the multi-aspect nature of decision making in complex AI. A decision algorithm of 32 rules grouped into five priority tiers is developed. Testing on three representative scenarios demonstrates three distinct topologies of the solution graph – from a degenerate case to a full bipartite one. The results confirm the correctness of mivar logical inference and the scalability of the knowledge base without any change to the neural-network module.

1. Jain A.K., Ross A.A., Nandakumar K. Introduction to Biometrics. New York: Springer; 2011. 312 p. https://doi.org/10.1007/978-0-387-77326-1

2. Garcez A.D., Lamb L.C. Neurosymbolic AI: the 3rd wave. Artificial Intelligence Review. 2023;56(11):12387–12406. https://doi.org/10.1007/s10462-023-10448-w

3. Wu W., Elliott S.J., Lin S., et al. Review of palm vein recognition. IET Biometrics. 2020;9(1):1–10. https://doi.org/10.1049/iet-bmt.2019.0034

4. Das R., Piciucco E., Maiorana E., et al. Convolutional Neural Network for Finger-Vein-Based Biometric Identification. IEEE Transactions on Information Forensics and Security. 2019;14(2):360–373. https://doi.org/10.1109/TIFS.2018.2850320

5. Huang H., Liu Sh., Zheng H., et al. DeepVein: Novel finger vein verification methods based on deep convolutional neural networks. In: 2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), 22–24 February 2017, New Delhi, India. IEEE; 2017. https://doi.org/10.1109/ISBA.2017.7947683

6. Sarker I.H. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science. 2021;2(6):420. https://doi.org/10.1007/s42979-021-00815-1

7. Kumar A., Zhou Y. Human Identification Using Finger Images. IEEE Transactions on Image Processing. 2012;21(4):2228–2244. https://doi.org/10.1109/TIP.2011.2171697

8. Varlamov O.O. Evolutionary databases and knowledge bases for adaptive synthesis of intelligent systems. Mivar information space. Moscow: Radio i svyaz; 2002. 286 p. (In Russ.).

9. Varlamov O., Aladin D. A New Generation of Rules-based Approach: Mivar-based Intelligent Planning of Robot Actions (MIPRA) and Brains for Autonomous Robots. Machine Intelligence Research. 2024;21(5):919–940. https://doi.org/10.1007/s11633-023-1473-1

10. Varlamov O.O. Mivar technologies: transition from productions to bipartite graphs mivar nets and realization of automated constructor of algorithms handling more than three million production rules. Artificial Intelligence. 2012;(4):11–33. (In Russ.).

11. Pizer S.M., Amburn E.Ph., Austin J.D., et al. Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing. 1987;39(3):355–368. https://doi.org/10.1016/S0734-189X(87)80186-X

12. He K., Zhang X., Ren Sh., et al. Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27–30 June 2016, Las Vegas, USA. IEEE; 2016. P. 770–778. https://doi.org/10.1109/CVPR.2016.90

13. Schroff F., Kalenichenko D., Philbin J. FaceNet: A Unified Embedding for Face Recognition and Clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 07–12 June 2015, Boston, USA. IEEE; 2015. P. 815–823. https://doi.org/10.1109/CVPR.2015.7298682

14. Obayya M.I., El-Ghandour M., Alrowais F. Contactless Palm Vein Authentication Using Deep Learning With Bayesian Optimization. IEEE Access. 2020;9:1940–1957. https://doi.org/10.1109/ACCESS.2020.3045424

15. Deng J., Guo J., Xue N., et al. ArcFace: Additive Angular Margin Loss for Deep Face Recognition. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 15–20 June 2019, Long Beach, USA. IEEE; 2019. P. 4685–4694. https://doi.org/10.1109/CVPR.2019.00482