Reflection-type Finger Vein Recognition for Mobile Applications

Recently, finger vein sensors have been embedded in all kinds of electronic devices for personal identification, such as intelligent door locks and attendance machines. The embedded sensors are generally small, thus capturing only part of the finger vein. However, prior studies have focused on near-full finger vein recognition, without considering the partial finger vein image caused by the small imaging window of the finger vein sensor. This paper aims to study personal identification based on partial finger vein images, known as small-area finger vein recognition. The effect of the small-area finger vein on recognition performance is first analyzed by cutting out the local part from the near-full finger vein image to model a small-area finger vein image. Second, a small-area finger vein database is built using a commercial finger vein imaging device, in which the vein pattern from approximately one-third of one adult finger is captured. To explore more discriminative information from small-area finger vein images, we propose a locality-constrained consistent dictionary learning (LCDL) method to fuse multiple features for small-area finger vein recognition. Finally, the proposed method is evaluated on the self-built small-area finger vein database and four synthetic small-area finger vein databases. Experimental results show the promising recognition performance of the proposed method.

Exploring finger vein based personal authentication for secure IoT

To carry out the finger vein recognition effectively and quickly, according to the characteristics of horizontal and vertical two-dimensional linear discriminant analysis ((2D)2LDA) reducing the dimensions, an algorithm of finger vein recognition is proposed. The algorithm is two direction weighted (2D)2LDA ((W2D)2LDA) based on preprocessing image of the figure vein image. Firstly, the original finger vein image is pre-processed. Secondly, the normalized vein image is gotten. Finally, the algorithm of this paper is used to be recognized. The effect of the rate of cumulate eigenvalue for 2DLDA and (2D)2LDA is analyzed. The effect of the weighted value and the rate of cumulate eigenvalue on W2DLDA, W(2D)2LDA and (W2D)2LDA are analyzed as well. Experimental results on our database of finger vein images show that the method presented achieves high recognition accuracy. The redundant information of eigenvectors extracted by (2D)2LDA is restrained strongly, and the bi-direction weighted effect is better than the one direction weighted effect. The average recognition rate of (W2D)2LDA is higher than 2DLDA, W2DLDA, (2D)2LDA and W(2D)2LDA.

Finger vein recognition is a promising biometric authentication technique that depends on the unique features of vein patterns in the finger for recognition. The existing finger vein recognition methods are based on minutiae features or binary features such as LBP, LLBP, PBBM etc. or from the entire vein pattern. However, the minutiae-based features cannot accurately represent the structural or anatomical aspects of the vein pattern. These issues with the minutia feature led to increased false matches. Recognition based on binary features have limitations such as increased false matches, sensitivity to the translation and rotation, security and privacy issues etc. A feature representation based on the anatomy of vein patterns can be an alternative solution to improve the recognition performance. In the IJCB 2020 conference, we showed that every finger vein image contains one or more of a kind of 4 special vein patterns which we refereed as Fork, Eye, Bridge, and Arch (FEBA). In this paper, we further enlarge this set to 6 vein patterns (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> EB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> A) by identifying two variations in the Fork and Bridge vein patterns. Based on 6 anatomical features of the possible 6 vein patterns in a vein image, we define a 6 × 6 feature matrix representation for finger vein images. Since this feature representation is based on the anatomical properties of the local vein patterns, it provides template security. Further we show that, the proposed feature representation is invariant to scaling, translation, and rotation changes. The experimental results using two open datasets and an in-house dataset show that the proposed method has a better recognition performance when compared to the existing approaches with an EER around 0.02% and an average recognition accuracy of 98%.

Finger vein recognition has become a research hotspot in the field of biometrics due to its advantages of non-contact acquisition, unique information, and difficulty in terms of forging or pirating. However, in the real-world application process, the extraction of image features for the biometric remains a significant challenge when the captured finger vein images suffer from blur, noise, or missing feature information. To address the above challenges, we propose a novel deep reinforcement learning-based finger vein image recovery method, DRL-FVRestore, which trained an agent that adaptively selects the appropriate restoration behavior according to the state of the finger vein image, enabling continuous restoration of the image. The behaviors of image restoration are divided into three tasks: deblurring restoration, defect restoration, and denoising and enhancement restoration. Specifically, a DeblurGAN-v2 based on the Inception-Resnet-v2 backbone is proposed to achieve deblurring restoration of finger vein images. A finger vein feature-guided restoration network is proposed to achieve defect image restoration. The DRL-FVRestore is proposed to deal with multi-image problems in complex situations. In this paper, extensive experimental results are conducted based on using four publicly accessible datasets. The experimental results show that for restoration with single image problems, the EER values of the deblurring network and damage restoration network are reduced by an average of 4.31% and 1.71%, respectively, compared to other methods. For images with multiple vision problems, the EER value of the proposed DRL-FVRestore is reduced by an average of 3.98%.

Recent advancements in finger vein recognition technology: Methodology, challenges and opportunities

Recent years have seen a plenty of personal identification methods with different biometrics such as finger pattern, face, palm-print and vein. The majority of these methods focus on complex image data projections and transforms in Fourier space, wavelet space or other domains, which usually bring heavy load in computation and difficult understanding in perceptual intuition. Moreover, these methods, oriented to multiple samples learning, are constricted usually in application. Among so much biometrics, vein, as a living feature with high anti-counterfeiting capability, has attracted considerable attention. In this paper, we propose a structured personal identification approach using finger vein Location and Direction Coding(LDC). First of all, we design a finger vein imaging device with near-infrared(NIR) light source, by which a database for finger vein images is established. Subsequently, we make use of the brightness difference in the finger vein image to extract the vein pattern. Furthermore, finger vein LDC is proposed and performed, which creates a structured feature image for each finger vein. Finally, the structured feature image is utilized to conduct the personal identification on our image database for finger vein, which includes 440 vein images from 220 different fingers. The equal error rate of our method for this database is 0.44%.

Finger vein recognition has become a new hot spot for its superior security features and user acceptance in recent years. According to the features of human finger vein image, a finger vein feature extraction method based on improved adaptive Niblack threshold segmentation algorithm is proposed. In this method, Niblack window parameter and the correction factor are given. In order to finish sufficient extraction of the finger vein feature, the image is enhanced by adaptive histogram equalization and filtering before feature extraction. The experimental results show that this method is simple and easy to realize and has laid a good foundation for the latter part of the vein recognition.

Finger vein recognition has received more attention in recent years due to its high security and promising development potential. However, extracting complete vein patterns and obtaining features from the original images suffer from the low contrast of finger vein images, which dramatically restrains the performance of finger vein recognition algorithms. Inspired by this motivation, we propose an explicit and implicit feature fusion Network (EIFNet) for finger vein verification. It can extract more comprehensive and discriminative features by complementarily fusing the features extracted from binary vein masks and gray original images. We design a feature fusion module (FFM) acting as a bridge between mask feature extraction module (MFEM) and contextual feature extraction module (CFEM) to achieve the optimal fusion of features. To obtain more accurate vein masks, we develop a novel finger vein pattern extraction method and provide the first finger vein segmentation dataset THUFVS. We solve the difficulty of building finger vein segmentation datasets in a simple but effective way, and develop a complete process encompassing dataset creation, data augmentation refinement and network design, which refers to the Mask Generation Module (MGM), for the deep learning based finger vein pattern extraction method. Experimental results demonstrate the superior verification performance of EIFNet on three widely used datasets compared with other existing methods.

Finger vein verification is a promising biometric pattern for personal identification in terms of security and convenience. The recognition performance of this technology heavily relies on the quality of finger vein images and on the recognition algorithm. To achieve efficient recognition performance, a special finger vein imaging device is developed, and a finger vein recognition method based on sparse representation is proposed. The motivation for the proposed method is that finger vein images exhibit a sparse property. In the proposed system, the regions of interest (ROIs) in the finger vein images are segmented and enhanced. Sparse representation and sparsity preserving projection on ROIs are performed to obtain the features. Finally, the features are measured for recognition. An equal error rate of 0.017% was achieved based on the finger vein image database, which contains images that were captured by using the near-IR imaging device that was developed in this study. The experimental results demonstrate that the proposed method is faster and more robust than previous methods.

Among the many biometric recognition techniques, finger vein recognition has many advantages, such as in-vivo recognition, high anti-counterfeiting, high acceptability, and high stability, etc., but the structure of finger vein is simple and the available information is less. The traditional feature extraction method is not fully used to extract finger vein structure information. In this paper, discrete separable shearlet transformation (DSST) are used to extract finger vein features. DSST is used to decompose the image of finger vein, and the decomposition coefficient is obtained and analyzed. The experimental results show that this method can provide an effective and sparse feature description of the original finger vein image, and also obtain a good recognition effect when the quality of some images is poor.

Aiming at the problems of traditional sparse representation algorithms that has a large amount of calculation and low recognition rate under the condition of small samples, a sparse representation classification algorithm (PCA-SRC) combined with principal component feature extraction is proposed. The algorithm can maintain the manifold structure of the finger vein space, solve the singularity problem of the characteristic equation matrix in small sample image recognition. Principal component analysis (PCA) is used to reduce the dimension of the feature matrix to eliminate redundancy. Construct an over-complete dictionary of sparse representation, and design a classifier based on sparse representation to get the classification results. The finger vein feature matching experiment is carried out using the self-built finger vein data set, and the algorithm proposed in this paper is compared with the traditional algorithm. The results show that the recognition rate of the four algorithms can reach more than 94%. The fusion of PCA and SRC speeds up the recognition speed. The recognition rate is higher than the traditional algorithm under different numbers of training samples, and the best recognition rate is 100%. The algorithm has high finger vein recognition rate and low dependence on the training sample set, which provides a new idea for the realization of finger vein recognition.

Finger vein recognition is a biometric technology using finger veins to authenticate a person, and due to its high degree of uniqueness, liveness, and safety, it is widely used. The traditional Symmetric Local Graph Structure (SLGS) method only considers the relationship between the image pixels as a dominating set, and uses the relevant theories to tap image features. In order to better extract finger vein features, taking into account location information and direction information between the pixels of the image, this paper presents a novel finger vein feature extraction method, Multi-Orientation Weighted Symmetric Local Graph Structure (MOW-SLGS), which assigns weight to each edge according to the positional relationship between the edge and the target pixel. In addition, we use the Extreme Learning Machine (ELM) classifier to train and classify the vein feature extracted by the MOW-SLGS method. Experiments show that the proposed method has better performance than traditional methods.

Finger vein recognition technology is an emerging biometric identification technology that utilizes the distribution structure of venous blood vessels to achieve identification. The vein recognition process is divided into two parts: registration and recognition. In the registration process, the generation of registration template is particularly important. In order to obtain the registration template more accurately, this paper proposes a finger vein image registration algorithm based on genetic algorithm. The principle of the algorithm is to use the mutual information of two finger vein images as the fitness function of the genetic algorithm, and use the genetic algorithm to search for the optimal parameters of the rigid body transformation model. The experimental results show that the algorithm is effective and can achieve the registration of finger vein images within a short iteration.

As an emerging biometric modality, finger vein recognition has received considerable attentions. However, recent studies have shown that finger vein biometrics is vulnerable to presentation attacks, i.e. printed versions of authorized individuals’ finger veins could be used to gain access to facilities or services. In this paper, we have designed a specific shallow convolutional neural network (CNN) for finger vein presentation attack detection (PAD), which is called as FPNet for short. The proposed FPNet has been evaluated on a public-database and an intra-database. Lots of h × h patches have been extracted from vein images with a stride s for dataset augmentation and then used to train our networks without any pre-trained model. For further improving models’ generalizability and robustness, training patches of two databases have been mixed together and our best model has achieved an accuracy of 100% on both test datasets, clearly outperforming state-of-the-art methods.