VeinKAN: A Finger Vein Recognition Model Based on Kolmogorov–Arnold Networks

• Published in: Applied Computer Systems
• Main Authors: Tran An Cong, Tran Nghi Cong
• Format: Article
• Language: English
• Published: Sciendo 2025-01-01
• Subjects: cnn; finger vein recognition; kolmogorov-arnold networks
• Online Access: https://doi.org/10.2478/acss-2025-0008

Finger vein recognition has become a secure biometric method known for its robustness against spoofing and environmental variations. Traditional methods, which often rely on Multi-layer Perceptrons (MLPs), face limitations in adaptability stemming from fixed activation functions and linear weight constraints. Kolmogorov–Arnold Networks (KANs) offer a novel architecture that enhances nonlinear learning capabilities to improve performance without significantly increasing computational overhead. This study proposes a KAN-based approach for finger vein recognition and evaluates its performance against established Convolutional Neural Network (CNN) models, including InceptionV3, EfficientNet, and MobileNetV3. Experiments on the FV_USM and SDUMLA-HMT benchmark datasets reveal that the proposed model achieves accuracies of 99.3 % and 96.2 %, respectively, surpassing conventional architectures. Despite a higher parameter count (34.81 million), the proposed model maintains an inference time of 1.0096 ms, which is comparable to InceptionV3 (1.006 ms) and notably faster than EfficientNet_B4 (1.349 ms). With a computational complexity of 539.12 MMAC, it supports the feasibility of biometric systems requiring high accuracy and efficient processing. These findings highlight KANs as a promising advancement in biometric recognition technologies.