Enhancing Finger Vein Classification through CLAHE and Sobel Filtering with Two Channel Hybrid Convolutional Machine Learning Algorithm

Advancements in digital technology have driven the rise of biometric security systems, notably in the field of finger vein detection. In most of the research on finger vein classification in the literature, achieving high accuracy is the main aim, while aspects such as generalization capacity and test distribution are mostly overlooked. In this study, two different datasets (MMCBNU_6000 and FV-USM) were tested with different test distributions, using a K-Fold structure for unbiased sampling in classification. In experiment part, two distinct image enhancement methods, namely Contrast Limited Adaptive Histogram Equalization (CLAHE) and Sobel filtering, were utilized on the datasets, and Convolutional Neural Networks (CNN) were used for feature extraction. Furthermore, machine learning algorithms were applied for classification, forming a Hybrid Convolutional Machine Learning algorithm. In this method, the model, which is fed through two different channels compared to conventional learning algorithms, combines classical machine learning classifiers with the CNN model. In the scope of this study, three tasks were outlined. The first two focused on implementing various machine learning algorithms for each dataset, while the third involved merging datasets and employing the Stacking Ensemble Classifier (SEC). For evaluating the models, accuracy and F1-score metrics were used. The results indicate that the highest accuracy rate was achieved in the third experiment, with a score of 98.94%. Additionally, it is also observed that increasing the amount of test data (the difference between 20% Test and 50% Test) has a minimal effect in reducing the model's accuracy metric compared to previous studies.