Finger Vein Database Research Articles

Finger-vein biometric identification using convolutional neural network

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.

Finger-Vein Image Enhancement Using a Fuzzy-Based Fusion Method with Gabor and Retinex Filtering

Because of the advantages of finger-vein recognition systems such as live detection and usage as bio-cryptography systems, they can be used to authenticate individual people. However, images of finger-vein patterns are typically unclear because of light scattering by the skin, optical blurring, and motion blurring, which can degrade the performance of finger-vein recognition systems. In response to these issues, a new enhancement method for finger-vein images is proposed. Our method is novel compared with previous approaches in four respects. First, the local and global features of the vein lines of an input image are amplified using Gabor filters in four directions and Retinex filtering, respectively. Second, the means and standard deviations in the local windows of the images produced after Gabor and Retinex filtering are used as inputs for the fuzzy rule and fuzzy membership function, respectively. Third, the optimal weights required to combine the two Gabor and Retinex filtered images are determined using a defuzzification method. Fourth, the use of a fuzzy-based method means that image enhancement does not require additional training data to determine the optimal weights. Experimental results using two finger-vein databases showed that the proposed method enhanced the accuracy of finger-vein recognition compared with previous methods.

Exploring soft biometric trait with finger vein recognition

Soft biometric trait has been used as ancillary information to enhance the recognition accuracy for face, fingerprint, gait, iris, etc. In this paper, we present a new investigation of soft biometric trait to improve the performance of finger vein recognition. We first propose some extraction criteria of soft biometric trait for comprehensively understanding this kind of ancillary information. And then based on these criteria, the width of phalangeal joint is employed as a novel soft biometric trait, which can be directly extracted from finger vein image. Finally, three frameworks are developed to conduct the combination of the width measurement and finger vein pattern, i.e., the fusion framework, the filter framework and the hybrid framework. We perform rigorous experiments both on the open and self-built finger vein databases, and experimental results illustrate that soft biometric trait can make promising improvement of finger vein recognition performance.

Biometric recognition using fuzzy score level fusion

Multi-modal biometric recognition uses more than one biometric identifier to recognise a person. Identification based on multiple templates becomes an emerging need. Multi-modal biometric systems are expected to be more reliable due to the presence of multiple independent pieces of biometric traits. This paper proposes a new approach for biometric recognition using hand vein and finger vein images. Our proposed method employs hyper analytic wavelet transform and automatic thresholding techniques to extract features from the images of hand. Once the veins are extracted the location and width are stored in the database. Here score level fusion based on fuzzy logic is utilised for recognition. This approach is tested on the finger vein and hand vein databases which is containing 500 samples from 100 users. The experimental results exhibits that the proposed method is comparable with the existing recognising methods.

Evaluate and Determine the Most Appropriate Method to Identify Finger Vein

In this paper, the performance of a variety of different methods of dimensionality reduction on finger vein database is evaluated to determine the most appropriate one in terms of finger vein recognition. Principal Component Analysis using K-nearest neighbor (KNN) as a classifier, different types of Kernel Principal Component Analysis (KPCA) using KNN as a classifier, different types of Kernel Entropy Component Analysis (KECA) using KNN as a classifier, and finally different types of KPCA using Local mean-based k-nearest centroid neighbor (LMKNCN) as a classifier are implemented on finger vein database. Different types of KPCA and KECA used in this experiment are Linear, Polynomial, and Gaussian. Extensive comparisons are made in this paper to identify which method matches finger vein recognition best.