Finger Vein Recognition Method Research Articles

Encoding Coefficient Similarity-Based Multifeature Sparse Representation for Finger Vein Recognition

Finger vein recognition is a promising biometric technology that has received significant research attention. However, most of the existing works often relied on a single feature, which failed to fully exploit the discriminative information in finger vein images, and therefore led to a limited recognition performance. To overcome this limitation, this paper proposes an encoding coefficient similarity-based multifeature sparse representation method for finger vein recognition. The proposed method not only uses multiple features to extract comprehensive information from finger vein images, but also obtains more discriminative information through constraints in the objective function. The sparsity constraint retains the key information of each feature, and the similarity constraint explores the shared information among the features. Furthermore, the proposed method is capable of fusing all kinds of features, not limited to specific ones. The optimization problem of the proposed method is efficiently solved using the alternating direction multiplier method algorithm. Experimental results on two public finger vein databases HKPU-FV and SDU-FV show that the proposed method achieves good recognition performance.

FFV-MBC: A Novel Fused Finger-Vein Recognition Method Based on Monogenic Binary Coding

<p>To improve pattern representation capabilities and robustness in traditional finger-vein recognition algorithms. In this paper, we propose FFV-MBC, a novel fused finger-vein recognition method based on monogenic binary coding (MBC). First of all, the amplitude, orientation, and phase information of the finger-vein images are filtered by a multi-scale monogenic log-Gabor filter and encoded by the binary coding theory. Three local features, MBC-A, MBC-P, and MBC-O, are achieved from different combinations of local image intensity and variation coding. After obtaining the features, we utilize the block-based Fisher Linear Discriminant method to reduce the dimension. Finally, the similarity components are calculated by the cosine distance and fused for the final finger-vein recognition results. We evaluate our proposed method on two publicly available datasets and one self-built dataset, i.e., Malaysian Polytechnic University (FV-USM), the Group of Machine Learning and Applications of Shandong University (SDUMLA-HMT), and our team, Signal and Information Processing Laboratory (FV-SIPL). On average, the proposed method achieved high recognition accuracy, i.e., 99.30%, and 1.10% equal error rates (EER). Overall, the proposed method performs better than most classical and state-of-the-art finger-vein recognition methods.</p> <p> </p>

Finger vein recognition based on bilinear fusion of multiscale features

Finger veins are widely used in various fields due to their high safety. Existing finger vein recognition methods have some shortcomings, such as low recognition accuracy and large model size. To address these shortcomings, a multi-scale feature bilinear fusion network (MSFBF-Net) was designed. First, the network model extracts the global features and local detail features of the finger veins and performs linear fusion to obtain second-order features with richer information. Then, the mixed depthwise separable convolution replaces the ordinary convolution, which greatly reduces the computational complexity of the network model. Finally, a multiple attention mechanism (MAM) suitable for finger veins was designed, which can simultaneously extract the channel, spatial, directional, and positional information. The experimental results show that the method is very effective, and the accuracy of the two public finger vein databases is 99.90% and 99.82%, respectively.

Finger Vein Recognition Based on Anatomical Features of Vein Patterns

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 method based on ant colony optimization and improved EfficientNetV2.

Deep learning is an important technology in the field of image recognition. Finger vein recognition based on deep learning is one of the research hotspots in the field of image recognition and has attracted a lot of attention. Among them, CNN is the most core part, which can be trained to get a model that can extract finger vein image features. In the existing research, some studies have used methods such as combination of multiple CNN models and joint loss function to improve the accuracy and robustness of finger vein recognition. However, in practical applications, finger vein recognition still faces some challenges, such as how to solve the interference and noise in finger vein images, how to improve the robustness of the model, and how to solve the cross-domain problem. In this paper, we propose a finger vein recognition method based on ant colony optimization and improved EfficientNetV2, using ACO to participate in ROI extraction, fusing dual attention fusion network (DANet) with EfficientNetV2, and conducting experiments on two publicly available databases, and the results show that the recognition rate using the proposed method on the FV-USM dataset reaches The results show that the proposed method achieves a recognition rate of 98.96% on the FV-USM dataset, which is better than other algorithmic models, proving that the method has good recognition rate and application prospects for finger vein recognition.

Notice of Removal: Robust and Efficient Finger Vein Recognition Using Binarized Difference of Gabor Jet on Grid

In practical applications, the biometric authentication system requires a lower error rate, higher speed, and smaller template size. To satisfy such requirements, we propose a robust and efficient finger vein recognition method, consisting of three stages: preprocessing, feature extraction, and comparison. During the preprocessing, geometric and photometric variations are suppressed. The feature extraction is based on a high-level descriptor derived from the binarized difference of Gabor jet (BDGJ) extracted from predefined grid points in the finger region. In the descriptor, the feature vectors at the vertices of the grid are weighted according to their position relative to the finger region. This descriptor provides a high discrimination ability with robustness. The comparison is conducted in a coarse-to-fine approach based on the Hamming distance, with remaining horizontal shifts and finger rolling compensated. The experimental results show that the proposed method has state-of-the-art performance and efficiency for practical applications. In particular, the method’s equal error rate (EER) is 0.00% on some famous public databases, and its computational cost is so low that the method can be run on a mobile processor with high speed.

A study on finger vein recognition with few samples based on residual connected meta‐learning

AbstractA meta‐learning based method for finger vein recognition with few samples is proposed to overcome the problem of low recognition accuracy caused by the small number and variety of finger vein samples as well as fuzzy vein lines. The method is based on meta‐learning, incorporating multiscale features, and using the idea of residual networks to join meta‐learning to improve the recognition accuracy of finger vein images with few samples; to further improve its recognition ability, a differential map is constructed in the form of a differential between the finger vein image of singular value decomposition and finger vein image. We are the first to apply meta‐learning to the field of finger vein recognition, to our knowledge, and the experiments show that this approach is superior, with recognition accuracy of up to 99.13% for finger vein datasets with few‐shot samples.

ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition

Finger vein recognition has been widely studied due to its advantages, such as high security, convenience, and living body recognition. At present, the performance of the most advanced finger vein recognition methods largely depends on the quality of finger vein images. However, when collecting finger vein images, due to the possible deviation of finger position, ambient lighting and other factors, the quality of the captured images is often relatively low, which directly affects the performance of finger vein recognition. In this study, we proposed a new model for finger vein recognition that combined the vision transformer architecture with the capsule network (ViT-Cap). The model can explore finger vein image information based on global and local attention and selectively focus on the important finger vein feature information. First, we split-finger vein images into patches and then linearly embedded each of the patches. Second, the resulting vector sequence was fed into a transformer encoder to extract the finger vein features. Third, the feature vectors generated by the vision transformer module were fed into the capsule module for further training. We tested the proposed method on four publicly available finger vein databases. Experimental results showed that the average recognition accuracy of the algorithm based on the proposed model was above 96%, which was better than the original vision transformer, capsule network, and other advanced finger vein recognition algorithms. Moreover, the equal error rate (EER) of our model achieved state-of-the-art performance, especially reaching less than 0.3% under the test of FV-USM datasets which proved the effectiveness and reliability of the proposed model in finger vein recognition.

Finger-vein feature extraction method based on vision transformer

Biometrics is widely used in life, and finger-vein recognition also has preliminary applications. Finger-vein recognition methods based on deep learning have achieved state-of-the-art performance, but there are still some challenges, such as sensitivity to finger posture, low recognition efficiency, etc. In our study, a data augmentation strategy using the random sliding window is designed to alleviate the problem caused by changes in finger posture. In addition, to improve the recognition efficiency while ensuring the accuracy, a lightweight finger-vein feature extraction model is proposed based on vision transformer, in which the tokens to token structure is able to describe the global correlation of images at multiple scales. Experimental results on three public databases indicate that compared with other deep-learning methods, the proposed method has achieved higher recognition efficiency and accuracy.

A finger-vein recognition method based on double-weighted group sparse representation classification

A finger-vein recognition method based on double-weighted group sparse representation classification

Multi-feature fusion partitioned local binary pattern method for finger vein recognition

Multi-feature fusion partitioned local binary pattern method for finger vein recognition

A Joint Bayesian Framework Based on Partial Least Squares Discriminant Analysis for Finger Vein Recognition

Finger vein recognition has attracted considerable attention from the biometric identification technology community owing to its convenience and security. Unlike most previous works only pay attention to one part of finger vein recognition, we propose a joint Bayesian framework in this paper, which is based on partial least squares discriminant analysis (PLS-DA). It involves three major stages: 1) robust feature description, 2) discriminative feature mapping, and 3) separative verification. In stage 1), we extract line responses and orientation of finger veins using a bank of Gabor filter, and histograms are constructed in local patches as primitive features. Subsequently, in stage 2), a discriminant feature mapping based the PLS-DA (PLS-DA-FM) method is proposed to project these primitive features into low-dimensional forms in a supervised manner. Thus, highly compact and discriminative features are obtained in this stage. Finally, in stage 3), we directly build a Bayesian model based on the joint distribution of finger vein feature pairs to measure the similarity between the features. Extensive experiments on five finger vein datasets demonstrate the superior performance of the proposed method to most state-of-the-art finger vein recognition methods.

Learning Compact Multirepresentation Feature Descriptor for Finger-Vein Recognition

Due to its high anti-counterfeiting and universality, the use of finger-vein pattern for identity authentication has recently attracted extensive attention in academia and industry. Despite recent advances in finger-vein recognition, most of the hand-crafted descriptors require strong prior knowledge, which may be ineffective in expressing its distinctiveness. In this paper, we present a novel compact multi-representation feature descriptor (CMrFD) with visual and semantic consistency, for finger-vein feature representation. Given the finger-vein images, we first form two-view representations to describe the informative vein features in local patches. Then, we jointly learn a feature transformation to map the two-view representations into discriminative binary codes. For the projection function, we linearly combine multi-view information and minimize the quantization error between the projected binary features and the original real-valued features. In terms of visual consistency, we minimize the Euclidean distance of each representation from the same class, at the same time, maximize the Euclidean distance from different classes in the projected space. Semantic consistency is used to ensure that similar images have compact multi-representation combined projection features. Lastly, we calculate the block-wise histograms as the final extracted features for finger-vein recognition. Experimental results on four widely used finger-vein databases demonstrate that the proposed method outperforms the state-of-the-art finger-vein recognition methods.

Ffv-Mbc: A Novel Fused Finger-Vein Recognition Method Based on Monogenic Binary Coding

Ffv-Mbc: A Novel Fused Finger-Vein Recognition Method Based on Monogenic Binary Coding

Finger-Vein Biometric Recognition: A Review

Finger-vein-based biometric technology has received wide attention and has made some positive achievements in improving the performance of personal identification. This paper intends to provide an overview on the existing finger-vein-based biometric methods from the four steps of finger-vein recognition methods, including image acquisition, image preprocessing, feature extraction, and matching. After brief introduction of image acquisition and preprocessing, the applications of feature extraction method are summarized as: template-based method, representation-based method, and learning-based method. Moreover, deep learning-based methods have been analyzed and discussed. To review and summarize these methods, case studies of the finger vein database are introduced to verify their effectiveness. Meanwhile, this paper discussed the challenges and prospects of finger vein recognition research, especially the three-dimensional finger vein recognition research, to guide the future research direction in this field.

Integration of Two-Dimensional Kernel Principal Component Analysis Plus Two-Dimensional Linear Discriminant Analysis with Convolutional Neural Network for Finger Vein Recognition

High complexity and low recognition rate are two common problems with the current finger vein recognition methods. To solve these problems, this paper integrates two-dimensional kernel principal component analysis (K2DPCA) plus two-dimensional linear discriminant analysis (2DLDA) (K2DPCA+2DLDA) into convolutional neural network (CNN) to recognize finger veins. Considering the row and column correlations of the finger vein image matrix and the classes of finger vein images, the authors adopted K2DPCA and 2DLDA separately for dimensionality reduction and extraction of nonlinear features in row and column directions, producing a dimensionally reduced compressed image without row or column correlation. Taking the dimensionally reduced compressed image as the input, the CNN was introduced to learn higher-level features, making finger vein recognition more accurate and robust. The public dataset of Finger Vein USM (FV-USM) Database was adopted for experimental verification. The results show that the proposed approach effectively overcome the common defects of original image feature extraction: the insufficient feature description, and the redundancy of information. When the training reached 120 epochs, the model basically realized stable convergence, with the loss approaching zero and the recognition rate reaching 97.3%. Compared with two-directional two-dimensional Fisher principal component analysis ((2D)2FPCA), our strategy, which integrates K2DPCA+2DLDA with CNN, achieved a very high recognition rate of finger vein images.

A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features.

Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easily degraded to low-contrast, blur, as well as serious noise disturbance. Therefore, how to extract more efficient and robust features from these low-quality FV images, remains to be addressed. In this paper, a novel feature extraction method of FV images is presented, which combines curvature and radon-like features (RLF). First, an enhanced vein pattern image is obtained by calculating the mean curvature of each pixel in the original FV image. Then, a specific implementation of RLF is developed and performed on the previously obtained vein pattern image, which can effectively aggregate the dispersed spatial information around the vein structures, thus highlight vein patterns and suppress spurious non-boundary responses and noises. Finally, a smoother vein structure image is obtained for subsequent matching and verification. Compared with the existing curvature-based recognition methods, the proposed method can not only preserve the inherent vein patterns, but also eliminate most of the pseudo vein information, so as to restore more smoothing and genuine vein structure information. In order to assess the performance of our proposed RLF-based method, we conducted comprehensive experiments on three public FV databases and a self-built FV database (which contains 37,080 samples that derived from 1030 individuals). The experimental results denoted that RLF-based feature extraction method can obtain more complete and continuous vein patterns, as well as better recognition accuracy.

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

In order to solve the problems of many model parameters during training, long training time and low recognition accuracy, a finger vein recognition method was proposed that uses multi-receptive field bilinear convolutional neural network (MRFBCNN). The network can obtain the second-order features of finger veins, and better distinguish finger veins with small differences between classifications. Then the lightweight neural network is used to reduce network parameters and computational complexity, so that the algorithm can be easily implemented in the hardware with limited resources. Finally, a dimensional interactive attention mechanism (DIAM) is designed to enhance the correlation between channels and spaces, and further improve the recognition accuracy. Experimental results show that this method not only improves the accuracy of finger vein recognition, but also reduces the training time and model parameters, so it is suitable for the practical application of finger vein recognition.

DGLFV: Deep Generalized Label Algorithm for Finger-Vein Recognition

With the growing demand for information security, finger-vein recognition has become widespread. However, the robustness of the recognition process becomes a major problem. When identifying unseen categories with traditional finger-vein recognition systems, a few issues remain, such as recognition interference and low efficiency. This paper proposes a Deep Generalized Label Finger-Vein (DGLFV) model to extract feature maps and achieve high-accuracy recognition. The largest rectangular finger-vein region is extracted through image semantic segmentation and the advanced bidirectional traversing and center diffusion method for the known categories. Then we generalize all the unseen categories actively as Class C+1 to reduce interference from unregistered users. Furthermore, an adaptive threshold acquisition algorithm is proposed for Label Receiver Operating Characteristic (LROC), so that the procedures of classification, recognition, and verification are unified. Apart from Shandong University Homologous Multi-modal Traits (SDUMLA-HMT), we have conducted additional experiments on our self-built database, Finger Veins of Signal and Information Processing Laboratory (FV-SIPL). The recognition accuracy of the approach proposed in this paper has reached 99.25% and 99.08% testing on FV-SIPL and SDUMLA-HMT, with a low error rate at 1.481% and 2.228% and little time consumption of 0.157s for a single image, which is better than most state-of-the-art finger-vein recognition methods.

Category-preserving binary feature learning and binary codebook learning for finger vein recognition

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.