Deep neural networks techniques for finger vein authentication

In this study, finger vein recognition technology has been focused to enhance security of biometric authentication. A new feature extraction technique has been proposed and applied on a dataset containing 3,816 finger-vein images from 106 individuals. This technique preserves the necessary data of images while decreasing the size and improve classification accuracy. It has multiple layers of neural networks that includes Conv1D, MaxPooling1D, LeakyReLU, and Dense layer to form a model. The results indicated that this model was very effective in detecting the venous structure having 27 layers with accuracy of 99.99%, a recall rate of 99.99%, an F1-score of 99.99% and Precision rate of 99.99%. The study indicated the potentialities and opportunities in developing new biometric and secure systems through the use of finger-vein recognition technology.