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Abstract

Finger vein authentication is a biometric technique that uses unique vein patterns within the finger to verify identity, ensuring security by scanning veins with near-infrared light to prevent counterfeiting. The reliability of this technology and its resistance to skin disorders make it highly appreciated. The motivation for using PCANet deep learning in finger vein authentication arises from the need for a more precise, secure, and effective biometric system. Conventional methods have trouble with image quality, noise, and illumination, whereas PCANet improves classification accuracy by employing principal component analysis (PCA) to extract deep features. Its lightweight structure guarantees computational efficiency while maintaining a high level of security, making it ideal for applications that require robust authentication. By using high-quality data and lowering the computational complexity needed by the PCANet approach, the suggested system seeks to increase performance. This research introduces three approaches: high-frequency emphasis filtering (HFEF), gabor filter (GF) images, and novel enhanced image techniques using contrast-limited adaptive histogram equalization (CLAHE) to keep edges and increase image quality. Second, for dimensionality reduction and feature extraction, a novel integrated PCANet as well as feature-level principal component analysis (PCA) is suggested. Ultimately, a pair of authentication models are chosen: one employing hyperparameter machine learning (HPML) in conjunction with Multi-Layer Perceptron (MLP), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) for random search, and the other using the same techniques for simple search. PLUSVein-Contactless finger vein dataset was used for evaluating such techniques. The suggested approaches performed better on this dataset, with an F1 score of some metrics of 100% for SVM, 99.90% for KNN, and 100% for MLP. Those results were for the random search model with PCA. The outcomes demonstrated a considerable improvement in F1-score of finger vein authentication using PCANet DL, both without and with PCA. Furthermore, the suggested authentication mechanism outperformed previously suggested approaches, in terms of improved image quality, good vein-specific features, and obtaining the best set of optimal parameters for matching.

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