Finger Vein Feature Extraction Research Articles

Joint Discriminative Analysis With Low-Rank Projection for Finger Vein Feature Extraction

Joint Discriminative Analysis With Low-Rank Projection for Finger Vein Feature Extraction

Raspberry PI-Based Finger Vein Recognition System Using PCA NET

Despite simultaneously ignoring the intensity distribution that is formed by the finger tissue and, in some instances, processing it as background noise, the majority of finger vein feature extraction algorithms achieve satisfactory performance due to their ability to represent texture. This project makes use of two- directional two-dimensional Fisher Principal Component Analysis, also known as (2D) 2 FPCA, for feature extraction. This type of "noise" is presented as a novel soft biometric trait for improving finger vein recognition performance. In order to demonstrate that the intensity distribution that is formed by the finger tissue in the background can be extracted as a soft biometric trait for recognition, a comprehensive analysis of the finger vein imaging principle and the characteristics of the image are first presented.

A finger vein authentication system based on pyramid histograms and binary pattern of phase congruency

Finger vein recognition is a novel biometric method which is getting more attention recently because of its high security and convenience. However, there are still many interference while recognizing, such as uneven illumination caused by non-uniform near-infrared light source or image dislocation caused by finger position change. Therefore, this paper proposes a finger vein feature extraction strategy integrating binary pattern of phase congruency (BPPC) and pyramid of histograms of orientation gradients (PHOG). We first calculate the energy information of each part in different regions and represents it with binary pattern, and then analyze the shape and texture of finger vein with image pyramid of histogram, and finally fuses them to obtain better recognition efficiency. Our method considers the multi-scale features of finger vein images so that some localdisturbance like overexposure of light source or insufficient illumination can be ignored, and our method is suitable for most environments and finger vein collection equipment. We experimented on both public database and database built by our lab with recognition rate more than 98%, which confirmed that our method is effective and has good practical significance.

A finger vein feature extraction network fusing global/local features and its lightweight network

A finger vein feature extraction network fusing global/local features and its lightweight network

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 Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections.

In the field of biometric recognition, finger vein recognition has received widespread attention by virtue of its advantages, such as biopsy, which is not easy to be stolen. However, due to the limitation of acquisition conditions such as noise and illumination, as well as the limitation of computational resources, the discriminative features are not comprehensive enough when performing finger vein image feature extraction. It will lead to such a result that the accuracy of image recognition cannot meet the needs of large numbers of users and high security. Therefore, this paper proposes a novel feature extraction method called principal component local preservation projections (PCLPP). It organically combines principal component analysis (PCA) and locality preserving projections (LPP) and constructs a projection matrix that preserves both the global and local features of the image, which will meet the urgent needs of large numbers of users and high security. In this paper, we apply the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger vein database to evaluate PCLPP and add “Salt and pepper” noise to the dataset to verify the robustness of PCLPP. The experimental results show that the image recognition rate after applying PCLPP is much better than the other two methods, PCA and LPP, for feature extraction.

Finger Vein Recognition System Based on Convolutional Neural Network and Android

With the rapid development of science and technology, biotechnology has developed rapidly. Among the many biometric technologies, finger vein technology has the characteristics of vitality, portability, and non-replicability, so it is considered to be the most promising biometric technology. However, the accuracy of finger vein recognition is affected by the collection device, the surrounding temperature and the algorithm. The flaws cannot be applied to real life on a large scale. This paper designs a finger vein recognition system based on convolutional neural network and Android, which mainly includes the following three parts. First, the system hardware includes the design of the acquisition device, the selection of the core development board and the display screen. Second, the design of the entire system software architecture is based on the MVVM architecture, which ensures low coupling of the program and is easy for later expansion and maintenance. The software includes collection function, recognition function and administrator function. Finally, a lightweight neural network is proposed for finger vein feature extraction, and proposed a storage method based on MMKV to meet the real-time performance of the system.

Finger Vein Biometric Identification Using Discretization Method

Over the past years, finger vein identification has gaining increasing attention in biometrics. It has many advantages as compared to other biometrics such as living-body identification, difficult to counterfeit because it resides underneath the finger skin and noninvasiveness. Finger vein feature extraction plays an important role in finger vein identification. The performance of finger vein identification is highly depending on the meaningful extracted features from feature extraction process. However, most of the works focus on how to extract the individual features and not presenting the individual characteristic of finger vein patterns with systematic representation. This paper proposed an improved scheme of finger vein feature extraction method by adopting discretization method. The extracted features will be represented systematically way in order to make classification task easier and increase the identification accuracy rate. The experimental result shows that the accuracy rate of identification of the proposed framework using Discretization is above 98.0%.

Research on Technology of Finger Vein Pattern Recognition Based on FPGA

Finger vein patterns recognition as a new type of biometric recognition technology, the existing pattern of the finger vein recognition has registered and verified the disadvantage of slow speed. Therefore, in this paper, a finger vein feature extraction and matching recognition algorithm suitable for FPGA is proposed, and the hardware system of finger vein recognition is designed and implemented, including three modules: finger vein image acquisition device, image processing module and matching display module. Finally, the key algorithm is simulated and analyzed. It speeds up the registration and recognition of mobile phone veins.

Evaluation on Finger-Vein Identification Process

The finger vein demand system plays a vivacious development for individual ID and check. In constant time, this system is getting approval, as it gives a high security and comfort approach for individual authentication. In this paper we concentrated on the structure study on different advances which are checked with the game-plan of finger vein seeing affirmation and verification. Clarified steps are finger Vein Data checking, finger Veinpre-sorting out, finger Veinsegmentation, finger Veinfeature extraction and finger Vein outline and managing. In setting on the assessment, the perfect philosophy and system is seen, which will be a calming answer for finger vein endorsing.

Study of a Full-View 3D Finger Vein Verification Technique

Finger vein modality has unique advantages, allowing it to play an important role in biometrics. However, the approach to vein imaging and information acquisition typically adopted in current vein verification systems employs a monocular camera to acquire a single-view 2D vein image from only one side of the finger, which causes two problems: it acquires limited vein pattern information for verification, and it causes clear differences among samples of the same subject captured from different finger positions in contact-free mode. Both of these problems have adverse effects on system performance. In general, existing systems are more sensitive to positional variations of the finger, particularly those caused by pitch and roll movements. This concern remains a challenge despite considerable efforts to address it in recent years. To provide a fundamental solution to the above issues, we propose an entirely new system, which includes a software and hardware platform that collects a full-view of the vein pattern information from whole fingers with three cameras, a novel 3D reconstruction method to build the full-view 3D finger vein image, and a corresponding 3D finger vein feature extraction and matching strategy based on a lightweight convolutional neural network (CNN) with depthwise separable convolution. Experimental results demonstrate the potential of our proposed system and show that compared to the traditional single-view 2D mode of finger vein recognition, the new system both efficiently improves the recognition performance and simultaneously takes full advantage of additional valid information provided by the finger vein biometrics.

From Noise to Feature: Exploiting Intensity Distribution as a Novel Soft Biometric Trait for Finger Vein Recognition

Most finger vein feature extraction algorithms achieve satisfactory performance due to their texture representation abilities, despite simultaneously ignoring the intensity distribution that is formed by the finger tissue, and in some cases, processing it as background noise. In this paper, we exploit this kind of noise as a novel soft biometric trait for achieving better finger vein recognition performance. First, a detailed analysis of the finger vein imaging principle and the characteristics of the image are presented to show that the intensity distribution that is formed by the finger tissue in the background can be extracted as a soft biometric trait for recognition. Then, two finger vein background layer extraction algorithms and three soft biometric trait extraction algorithms are proposed for intensity distribution feature extraction. Finally, a hybrid matching strategy is proposed to solve the issue of dimension difference between the primary and soft biometric traits on the score level. A series of rigorous contrast experiments on three open-access databases demonstrates that our proposed method is feasible and effective for finger vein recognition.

Exploring finger vein based personal authentication for secure IoT

Personal authentication is getting harder and harder in the internet of things (IoT). Existing methods used for personal authentication, such as passwords and the two-factor authentication (2FA), are inadequate and ineffective due to human error and other attacks. To support more secure IoT, this paper proposes a finger vein based personal authentication method by exploring competitive orientations and magnitudes from finger vein images. Finger vein recognition has been proven to be a reliable and promising solution for biometric-based personal authentication. The stable and rich piecewise line features in finger vein images can be used to clearly represent finger vein patterns for personal authentication. In this paper, we propose an efficient local descriptor for finger vein feature extraction, namely the histogram of competitive orientations and magnitudes (HCOM). For a finger vein image, two types of local histograms are extracted and fused together to efficiently and adequately represent the competitive information: the histogram of competitive orientations (HCO) and the local binary pattern histogram generated from the image of competitive magnitudes (named as HCMLBP). The extensive experimental results from the application of the proposed method to the public finger vein database MMCBNU_6000, demonstrate that the proposed method outperforms state-of-the-art orientation coding (OC)-based methods and other commonly used local descriptors. Additionally, the proposed method can be used for finger vein image enhancement.

A Finger Vein Identification Method Based on Template Matching

New methods for extracting vein features from finger vein image and generating templates for matching are proposed. In the algorithm for generating templates, we proposed a parameter-templates quality factor (TQF) - to measure the quality of generated templates. So that we can use fewer finger vein samples to generate templates that meet the quality requirement of identification. The recognition accuracy of using proposed methods of finger vein feature extraction and template generation strategy for identification is 97.14%.

Finger Vein Recognition Based on Multi-Orientation Weighted Symmetric Local Graph Structure

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 Feature Extraction Method Based on Fuzzy Set Theory and Gray-scale Morphology

Finger Vein Feature Extraction Method Based on Fuzzy Set Theory and Gray-scale Morphology

Sliding Window-Based Region of Interest Extraction for Finger Vein Images

Region of Interest (ROI) extraction is a crucial step in an automatic finger vein recognition system. The aim of ROI extraction is to decide which part of the image is suitable for finger vein feature extraction. This paper proposes a finger vein ROI extraction method which is robust to finger displacement and rotation. First, we determine the middle line of the finger, which will be used to correct the image skew. Then, a sliding window is used to detect the phalangeal joints and further to ascertain the height of ROI. Last, for the corrective image with certain height, we will obtain the ROI by using the internal tangents of finger edges as the left and right boundary. The experimental results show that the proposed method can extract ROI more accurately and effectively compared with other methods, and thus improve the performance of finger vein identification system. Besides, to acquire the high quality finger vein image during the capture process, we propose eight criteria for finger vein capture from different aspects and these criteria should be helpful to some extent for finger vein capture.