Any Biometric system comprises five modules which are data acquisition, Pre-processing, feature extraction, matching and decision. Finger vein is another biometric innovation that contends with other ground-breaking biometrics modalities, for example, the face, palm print, fingerprint, iris and voice. Finger vein recognition is a biometric method used to analyze finger vein patterns of people for appropriate verification. The feature extraction module is very important in a biometric system. The extracted features perhaps include irrelevant and redundant features that can drive to the retreat of the performance of the biometric system. To solve this problem, an efficient feature selection scheme based on the Genetic Algorithm (GA) for Finger vein recognition is proposed. While feature extraction the work was divided into four scenarios based on the full feature, Principal Components Analysis (PCA) method for feature reduction, a hybrid of GA and PCA for feature reduction and selection, and GA for feature selection. The proposed method tested on two standard finger vein biometrics databases (SDUMLA-HMT and UTFV). The experimental results show that the proposed method gives the best results with high accuracy reached to 99.95% and 99.89595%

Finger-Vein, Biometrics, Genetic Algorithm, Feature Extraction, Gabor Filter, PCA, Correlation Coefficients, FAR, FRR

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