Finger Vein Recognition Based on Multi-Receptive Field Bilinear Convolutional Neural Network

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

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 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.

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.

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.

Exploring finger vein based personal authentication for secure IoT

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.

Local binary feature learning has attracted a lot of researches in image recognition due to its vital effectiveness. Generally, in the traditional local feature learning methods, a projection is learned to map the patches of image into binary features and then a codebook is generated by clustering the binary features with K-means clustering. However, these local feature learning methods, such as compact binary face descriptor and discriminative binary descriptor, ignore the category specific distributions of the original features during the feature learning process and use the real-valued clustering approach to generate the codebook, the discriminant of feature is degraded and the merits of binary feature are lost. To tack these problems, in this paper, we propose a novel category-preserving binary feature learning and binary codebook leaning (CPBFL-BCL) method for finger vein recognition. In CPBFL-BCL, the discrimination of learned binary features is generated by criteria of fisher discriminant analysis and category manifold preserving regularity during the feature learning process, and a novel binary clustering method based on K-means clustering is designed to generate binary codebook. Experimental results on recognition and retrieval tasks using two public finger vein databases are presented and demonstrate the effectiveness and efficiency of the proposed method over the state-of-the-art finger vein methods and a finger vein retrieval method.

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.

Finger vein recognition, which is a promising biometric method for identity authentication, has attracted significant attention. Considerable research focuses on transmission-type finger vein recognition, but this type of authentication is difficult to implement in mobile consumer devices. Therefore, reflection-type finger vein recognition should be developed. In the reflection-type vein recognition field, the majority of researchers concentrate on palm and palm dorsa patterns, and only a few pay attention to reflection-type finger vein recognition. Thus, this paper presents reflection-type finger vein recognition for biometric application that can be integrated into mobile consumer devices. A database is built to test the proposed algorithm. A novel method of region-of-interest localization for a finger vein image is introduced, and a scheme for effectively extracting finger vein features is proposed. Experiments demonstrate the feasibility of reflection-type finger vein recognition.

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.

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.

In this work, we propose an efficient joint Bayesian model that expanded with soft biometric traits for finger vein recognition, which is different from the existing mainstream finger vein recognition methods that extract features from the region of interest (ROI) of finger vein images. First, a two-branch convolutional neural networks (CNNs) is designed to extract feature simultaneously from finger shape images and ROI vein images, respectively, the aim of which is to extract more information from different parts of fingers. Then, two different features are fused by concatenating. Finally, a joint Bayesian recognition loss is used to train the proposed two-branch CNN which is to promote intra-class variance minimization and inter-class variation maximization. Further, in our framework, a end-to-end learning method of CNNs and joint Bayesian recognition loss is achieved to optimize the parameters. Experiments performed on two public finger vein databases demonstrate that the proposed model is efficient and achieves better performance than state-of-the-art methods.

The finger vein recognition system uses blood vessels inside the finger of an individual for identity verification. The public is in favor of a finger vein recognition system over conventional passwords or ID cards as the biometric technology is harder to forge, misplace, and share. In this study, the histogram of oriented gradients (HOG) features, which are robust against changes in illumination and position, are extracted from the finger vein for personal recognition. To further increase the amount of information that can be used for recognition, different instances of the finger vein, ranging from the index, middle, and ring finger are combined to form a multi-instance finger vein representation. This fusion approach is preferred since it can be performed without requiring additional sensors or feature extractors. To combine different instances of finger vein effectively, score level fusion is adopted to allow greater compatibility among the wide range of matches. Towards this end, two methods are proposed: Bayesian optimized support vector machine (SVM) score fusion (BSSF) and Bayesian optimized SVM based fusion (BSBF). The fusion results are incrementally improved by optimizing the hyperparameters of the HOG feature, SVM matcher, and the weighted sum of score level fusion using the Bayesian optimization approach. This is considered a kind of knowledge-based approach that takes into account the previous optimization attempts or trials to determine the next optimization trial, making it an efficient optimizer. By using stratified cross-validation in the training process, the proposed method is able to achieve the lowest EER of 0.48% and 0.22% for the SDUMLA-HMT dataset and UTFVP dataset, respectively.

Several deep neural networks have been introduced for finger vein recognition over time, and these networks have demonstrated high levels of performance. However, most current state-of-the-art deep learning systems use networks with increasing layers and parameters, resulting in greater computational costs and complexity. This can make them impractical for real-time implementation, particularly on embedded hardware. To address these challenges, this article concentrates on developing a lightweight convolutional neural network (CNN) named FV-EffResNet for finger vein recognition, aiming to find a balance between network size, speed, and accuracy. The key improvement lies in the utilization of the proposed novel convolution block named the Efficient Residual (EffRes) block, crafted to facilitate efficient feature extraction while minimizing the parameter count. The block decomposes the convolution process, employing pointwise and depthwise convolutions with a specific rectangular dimension realized in two layers (n × 1) and (1 × m) for enhanced handling of finger vein data. The approach achieves computational efficiency through a combination of squeeze units, depthwise convolution, and a pooling strategy. The hidden layers of the network use the Swish activation function, which has been shown to enhance performance compared to conventional functions like ReLU or Leaky ReLU. Furthermore, the article adopts cyclical learning rate techniques to expedite the training process of the proposed network. The effectiveness of the proposed pipeline is demonstrated through comprehensive experiments conducted on four benchmark databases, namely FV-USM, SDUMLA, MMCBNU_600, and NUPT-FV. The experimental results reveal that the EffRes block has a remarkable impact on finger vein recognition. The proposed FV-EffResNet achieves state-of-the-art performance in both identification and verification settings, leveraging the benefits of being lightweight and incurring low computational costs.