Contactless Palm Vein Authentication Using Deep Learning With Bayesian Optimization

Among various biometric methods, palm vein authentication has taken significant attention because of its uniqueness, stability and non-intrusiveness. In this paper, we propose a palm vein authentication model using convolutional neural networks (CNN), which is the most popular deep learning architecture and Bayesian optimization. First and foremost, region of interest (ROI) of the palm vein is extracted as an image and filtered by Jerman enhancement filter to enhance the gray levels of the vein patterns. The proposed CNN model allows different numbers of convolutional layers to be added to optimize the network structure. Furthermore, the model is trained with training data to extract the highly representative features of the different classes. The training process is performed at every objective function evaluation, each with a different network structure and training options using a Bayesian optimization algorithm to find the optimal network structure and training options in a search space of possible solutions. The CNN model serves as the palm vein template creator or feature extractor for our identification and verification experiments. Receiver operating characteristic (ROC) curve and equal error rate (EER) were plotted for evaluating the performance of the proposed model. Our proposed method attained an average identification accuracy of 99.4 % and average EER of 0.0683%, which outperforms state-of-the-art palm vein authentication approaches.