Finger-vein biometric identification using convolutional neural network

Abstract

A novel approach using a convolutional neural network (CNN) for finger-vein biometric identification is presented in this paper. Unlike existing biometric techniques such as fingerprint and face, vein patterns are inside the body, making them virtually impossible to replicate. This also makes finger-vein biometrics a more secure alternative without being susceptible to forgery, damage, or change with time. In conventional finger-vein recognition methods, complex image processing is required to remove noise and extract and enhance the features before the image classification can be performed in order to achieve high performance accuracy. In this regard, a significant advantage of the CNN over conventional approaches is its ability to simultaneously extract features, reduce data dimensionality, and classify in one network structure. In addition, the method requires only minimal image preprocessing since the CNN is robust to noise and small misalignments of the acquired images. In this paper, a reduced-complexity four-layer CNN with fused convolutional-subsampling architecture is proposed for finger-vein recognition. For network training, we have modified and applied the stochastic diagonal Levenberg{Marquardt algorithm, which results in a faster convergence time. The proposed CNN is tested on a finger-vein database developed in-house that contains 50 subjects with 10 samples from each finger. An identification rate of 100.00% is achieved, with an 80/20 percent ratio for separation of training and test samples, respectively. An additional number of subjects have also been tested, in which for 81 subjects an accuracy of 99.38% is achieved.