Two modality-based bi-finger vein verification system

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.

In this paper, we try to solve the problem that it is difficult to distinguish between the background and foreground regions in vein images, and improve the feature extraction of finger veins by using maximum curvature method (MCM), which is easily affected by noise. In this paper, we use the improved maximum curvature method to extract finger vein features. Different from the maximum curvature method(MCM), we try an enhancement mechanism to improve the anti-noise ability. Firstly, the improved mask filtering method is used to locate the finger region, which can effectively separate the foreground region from the background region. When it comes to feature extraction, the Gabor wavelet transform is used to remove the noise of the image, and the basic gray-level grouping (GLG) method is used to increase the contrast of the image. Finally, the vein width is obtained by the maximum curvature method. The method is based on two datasets, namely, MMCBNU_6000 finger vein database and SDUMLA-HMT finger vein database. Experiments show that the recognition rates of the two databases are 99.27% and 98.13% respectively.

Exploring finger vein based personal authentication for secure IoT

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 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.

In today’s information age, how to accurately identify a person’s identity and protect information security has become a hot topic of people from all walks of life. At present, a more convenient and secure solution to identity identification is undoubtedly biometric identification, but a single biometric identification cannot support increasingly complex and diversified authentication scenarios. Using multimodal biometric technology can improve the accuracy and safety of identification. This paper proposes a biometric method based on finger vein and face bimodal feature layer fusion, which uses a convolutional neural network (CNN), and the fusion occurs in the feature layer. The self-attention mechanism is used to obtain the weights of the two biometrics, and combined with the RESNET residual structure, the self-attention weight feature is cascaded with the bimodal fusion feature channel Concat. To prove the high efficiency of bimodal feature layer fusion, AlexNet and VGG-19 network models were selected in the experimental part for extracting finger vein and face image features as inputs to the feature fusion module. The extensive experiments show that the recognition accuracy of both models exceeds 98.4%, demonstrating the high efficiency of the bimodal feature fusion.

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.

Authentication is a major gateway towards exploring unsecure treasure of information and data. In today’s modern technology driven society requires a full proof validation and authentication mechanism. Henceforth, this paper discusses on the finger vein pattern extraction and processed based on dual mode authentication system. Typically, the finger vein print based authentication is saturated research field and thus the paper explores authentication based on the pattern rather than print. The technique develops a Dynamic Finger Vein Extraction for dis-Sampled Datasets (DFVdSD). The technique provides an internal level enhancive approach of understanding and processing the finger vein patterns. The process is then authenticated with dual model of signal validation, the patterns of each finger vein is further synchronized with user signal data. Thus making the system secure and unbreakable. The technique also evaluates techniques on untrained dataset classification.

In medical systems for patient's authentication, keeping biometric data secure is a general problem. Many studies have presented various ways of protecting biometric data especially finger vein biometric data. Thus, It is needs to find better ways of securing this data by applying the three principles of information security aforementioned, and creating a robust verification system with high levels of reliability, privacy and security. Moreover, it is very difficult to replace biometric information and any leakage of biometrics information leads to earnest risks for example replay attacks using the robbed biometric data. In this paper presented criticism and analysis to all attempts as revealed in the literature review and discussion the proposes a novel verification secure framework based confidentiality, integrity and availability (CIA) standard in triplex blockchain-particle swarm optimization (PSO)-advanced encryption standard (AES) techniques for medical systems patient's authentication. Three stages are performed on discussion. Firstly, proposes a new hybrid model pattern in order to increase the randomization based on radio frequency identification (RFID) and finger vein biometrics. To achieve this, proposed a new merge algorithm to combine the RFID features and finger vein features in one hybrid and random pattern. Secondly, how the propose verification secure framework are followed the CIA standard for telemedicine authentication by combination of AES encryption technique, blockchain and PSO in steganography technique based on proposed pattern model. Finally, discussed the validation and evaluation of the proposed verification secure framework.

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.

In this paper, we propose a novel finger vein based cancelable biometric key generation scheme. Three main steps are involved in the proposed scheme: (i) Finger vein feature is extracted by maximum margin locality preserving projection (MMLPP); (ii) Chaotic random projection was used for cancelable biometric template generation; (iii) Construct a fuzzy commitment based key generation system. Experiment and analysis show our proposed scheme is a secure and efficient key generation scheme.KeywordsFinger VeinMaximum Margin Locality Preserving ProjectionChaotic Random ProjectionFuzzy CommitmentCancelable Biometric key

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.

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.

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.

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.