Review of Palm Vein Biometric Recognition Using Image Processing Techniques

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 1

Year of Publication : 2025

Authors : Suhas Chate, Vijay Patil, Yuvraj Parkale

10.14445/23488379/IJEEE-V12I1P108

How to Cite?

Suhas Chate, Vijay Patil, Yuvraj Parkale, "Review of Palm Vein Biometric Recognition Using Image Processing Techniques," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 1, pp. 76-93, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I1P108

Abstract:

The unique vascular patterns under the palm’s surface have led to the emergence of palm vein biometrics as a reliable, contactless and secure biometric identification method. The strength of this technology over traditional biometrics is that it is immune to forgery, stable over time and can detect if a live user is present or not. Recent advances have improved Palm Vein Recognition (PVR) systems in imaging principles, preprocessing, feature extraction, and classification. This modality has high accuracy, robustness to environmental factors, and resistance to forgery. It thus offers an attractive alternative to conventional biometric systems, such as face or iris recognition and fingerprinting. Near-Infrared (NIR) imaging makes non-contact and hygienic data acquisition even more appropriate for use in high-security standards fields such as financial transactions, healthcare and border control. The review presents an in-depth state-of-the-art technique, including the use of Machine Learning (ML) algorithms, multi-spectral imaging, and feature extraction methods. It also addresses the challenges related to computational complexity and template security. Future directions highlight the potential of integrating advanced algorithms, 3D imaging, and privacy-preserving methods for different applications in diverse domains.

Keywords:

Palm vein recognition, Biometric authentication, Near-Infrared (NIR) imaging, Feature extraction, Deep Learning models.

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