Contactless Palm Vein Recognition System with Integrated Learning Approach System

Palm Vein Recognition (PVR) is a new biometric authentication technology that provides both security and convenience. This paper describes a contactless PVR system (CPVR) that uses an integrated learning approach (ILA) to recognise the palm veins from the given input images while ensuring user comfort and ease of use. Contactless palm vein scanning technology is used in the proposed system, eliminating the need for physical contact with the scanning device. The proposed method combines advanced feature extraction techniques with a light gradient boosting machine (LightGBM) and transfer learning. A pre-trained model, EfficientNetB1, is used to train the model to extract significant factors from the input PVR images. The proposed method improves user comfort and reduces the risk of cross-contamination in environments where hygiene is critical, such as hospitals, banking, and other secured places. The cutting-edge contactless palm vein scanner captures the unique vein patterns beneath the user's palm without requiring direct physical contact. The proposed ILA illuminates and captures vein patterns using near-infrared (NIR) light, ensuring high accuracy and robustness. The system employs advanced pre-processing techniques and enhanced image segmentation techniques to continuously improve recognition accuracy. It adjusts to changes in the user's vein patterns over time, considering factors like ageing and injuries. The ILA improves the system's ability to adjust palm positioning and lighting changes. The ILA is also a Contactless Palm Vein Recognition System with numerous applications, such as access control, secure authentication for financial transactions, healthcare record access, and more. The system is built to be scalable, allowing organisations to use it in various settings, ranging from small-scale installations to large enterprise-level deployments. Finally, the proposed approach ILA used to recognise accurate users increased the detection rate.