EPVM: An efficient privacy-preserving palm vein model for user authentication
Divya Singla, Neetu Verma · Biomedical Signal Processing and Control · 2025
This research introduces a revolutionary palm vein authentication mechanism that achieves a significant level of accuracy and privacy. To achieve accuracy, a novel mix of preprocessing techniques (ROI, modified adaptive filter, Histogram equalization, skeletonization) are applied on various classification models (Decision tree, Random Forest, Principal Component Analysis-Support Vector Machine (PCA-SVM), Principal Component Analysis- Random Forest (PCA-RF), Partial Least Square-Regression (PLS-R)). The experimental findings demonstrate that PLS-R outperforms other models. For further improvement, PLS-R model is expanded and iterated over several components to find the best performing model based on certain criteria (e.g., accuracy, variance explained). To attain privacy, distance matrix of optimized PLS-R model is calculated during the training phase and generated secured template for authentication. Compared with state-of-the-art technique, our technique can ensure accuracy and privacy in biometric authentication by using feature reduction, distance-matrix computation, and threshold-based decision making for identity verification. Overall, the proposed model provides a secure authentication mechanism with high accuracy, and the privacy preservation of biometric traits.