Enhancing Finger Vein Authentication through Deep Learning: A Comparative Study of U-Net and Sequential Models

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

The integration of finger vein authentication technology to enhance security measures within restricted zones in hospital environments, is the aim of this project. An advanced biometric technique called finger vein authentication uses each person's individual vein patterns to confirm their identification. It is especially desirable for applications where security is critical, such financial transactions, healthcare, and access control, because it provides a very safe and non-intrusive means of authentication. Although there has been considerable success with traditional finger vein authentication methods, deep learning techniques have completely changed this field. A branch of artificial intelligence called deep learning has shown impressive results in pattern recognition and picture analysis, which makes it a prime contender for improving the precision and dependability of finger vein authentication systems. Finger vein authentication utilizes biometric characteristics unique to everyone, providing a highly secure method of identity verification. By implementing this technology, hospitals can bolster access control, prevent unauthorized entry into sensitive areas, and safeguard patient information and critical resources. The project aims to implement finger vein authentication systems to regulate access to restricted areas such as operating rooms, medication storage areas, and patient information sections. Authorized personnel will undergo authentication via finger vein scans for entry, ensuring that only authorized individuals can access these zones.

The necessity for personal data protection has become increasingly critical in recent years. In this case, an identification system based on multimodal biometric integration is the best option for greatly enhancing and obtaining high performance accuracy. Finger vein is considered an extremely secure and reliable biometric modality used for authentication. Finger vein authentication is a recently developed biometric technology that has gained more attention in the past decade. The traditional methods of finger vein authentication techniques are affected by several factors, such as illumination variations and image misalignments, that lead to inefficient feature extraction. As a result, the authentication system’s overall performance suffers. A convolutional neural network (CNN) is a deep learning–based solution to finger vein authentication that solves the problems that older methods have. This research proposes a deep learning–based feature extraction methodology for finger vein pictures. A convolutional layer, a rectified linear unit (ReLU), batch normaliation, and average pooling layer, followed by a fully connected layer and a softmax layer, make up the proposed CNN. Using a separate collection of images from the SDUMLA finger vein database, the CNN is trained and evaluated, and the suggested approach achieves an overall accuracy of 99.84%.

Most finger vein authentication systems suffer from the problem of small sample size. However, the data augmentation can alleviate this problem to a certain extent but did not fundamentally solve the problem of category diversity. So the researchers resort to pre-training or multi-source data joint training methods, but these methods will lead to the problem of user privacy leakage. In view of the above issues, this paper proposes a federated learning-based finger vein authentication framework (FedFV) to solve the problem of small sample size and category diversity while protecting user privacy. Through training under FedFV, each client can share the knowledge learned from its user’s finger vein data with the federated client without causing template leaks. In addition, we further propose an efficient personalized federated aggregation algorithm, named federated weighted proportion reduction (FedWPR), to tackle the problem of non-independent identically distribution caused by client diversity, thus achieving the best performance for each client. To thoroughly evaluate the effectiveness of FedFV, comprehensive experiments are conducted on nine publicly available finger vein datasets. Experimental results show that FedFV can improve the performance of the finger vein authentication system without directly using other client data. To the best of our knowledge, FedFV is the first personalized federated finger vein authentication framework, which has some reference value for subsequent biometric privacy protection research.

Finger vein identification using Convolutional Neural Network and supervised discrete hashing

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.

A review is conducted to deeply analyse and map the research landscape of current technologies in finger vein (FV) biometric authentication in medical systems into a coherent taxonomy. This research focuses on articles related to the keywords `biometrics', `finger veins' and `verification' and their variations in three major databases, namely, Web of Science, ScienceDirect and IEEE Xplore. The final set of collected articles related to FV biometric authentication systems is divided into software- and hardware-based systems. In the first category, software development attempts are described. The experiment results, frameworks, algorithms and methods that perform satisfactorily are presented. Moreover, the experiences obtained from conducting these studies are discussed. In the second category, hardware development attempts are described. The final articles are discussed from three aspects, namely, (1) number of publications, (2) problem type, proposed solutions, best results and evaluation methods in the included studies and (3) available databases containing different scientific work collected from volunteers, such as staff and students. The basic characteristics of this emerging field are identified from the following aspects: motivations of using FV biometric technology in authentication systems, open challenges that impede the technology's utility, authors' recommendations and future research prospects. A new solution is proposed to address several issues, such as leakage of biometrics that leads to serious risks due to the use of stolen FV templates and various spoofing and brute-force attacks in decentralised network architectures in medical systems, including access points and various database nodes without a central point. This work contributes to literature by providing a detailed review of feasible alternatives and research gaps, thereby enabling researchers and developers to develop FV biometric authentication medical systems further. Insights into the importance of such a technology and its integration into different medical applications and fields are also provided.

Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two decades. Multiple biometrics such as fingerprint, palm, iris, palm vein and finger vein and other biometrics have been introduced. One of the challenges in biometrics is physical injury. Biometric of finger vein is of the biometrics least exposed to physical damage. Numerous methods have been proposed for authentication with the help of this biometric that suffer from weaknesses such as high computational complexity and low identification rate. This paper presents a novel method of scattering wavelet-based identity identification. Scattering wavelet extracts image features from Gabor wavelet filters in a structure similar to convolutional neural networks. What distinguishes this algorithm from other popular feature extraction methods such as deep learning methods, filter-based methods, statistical methods, etc., is that this algorithm has very high skill and accuracy in differentiating similar images but belongs to different classes, even when the image is subject to serious damage such as noise, angle changes or pixel location, this descriptor still generates feature vectors in a way that minimizes classifier error. This improves classification and authentication. The proposed method has been evaluated using two databases Finger Vein USM (FV-USM) and Homologous Multi-modal biometrics Traits (SDUMLA-HMT). In addition to having reasonable computational complexity, it has recorded excellent identification rates in noise, rotation, and transmission challenges. At best, it has a 98.2% identification rate for the SDUMLA-HMT database and a 96.1% identification rate for the FV-USM database.

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems.
 
 © 2018 JASET, International Scholars and Researchers Association
 

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.

The modern-day finger vein based human recognition techniques provide good performance, yet they are highly finger vein image quality dependent. To address this problem, a novel deep learning-based approach using convolution-neural-network (CNN) for finger vein identification has been introduced here. The prime objective of our work is to achieve a stable response with accurate performance keeping varying quality finger vein images in account. The proposed approach is tested on the considered publicly available dataset and reported experiment results show that with effective training and testing strategy high identification accuracy can be achieved.

Nowadays personal information security is an important issue. Number of valuable data and files are stored in server and moreover private data are shared in worldwide. To access the private information only by the authorized user is becoming indispensable. Biometric plays a big role in strong security and large attention in research field. The biometric characteristics like palm print, finger print, Iris, DNA, face detection, finger vein etc. are used. Now finger vein is leading technique now. The big advantages of finger vein pattern are lively detection, present under the skin, and cant possible to copy or stolen. Finger vein authentication base applications are widely used in Japan’s like bank, airport, person authentication, jail etc. This paper, we study comparatively the various techniques of finger vein identification or authentication. The main focus is comparative study of existing vein pattern acquisition methods, feature extraction techniques and its results.

Advancements in digital technology have driven the rise of biometric security systems, notably in the field of finger vein detection. In most of the research on finger vein classification in the literature, achieving high accuracy is the main aim, while aspects such as generalization capacity and test distribution are mostly overlooked. In this study, two different datasets (MMCBNU_6000 and FV-USM) were tested with different test distributions, using a K-Fold structure for unbiased sampling in classification. In experiment part, two distinct image enhancement methods, namely Contrast Limited Adaptive Histogram Equalization (CLAHE) and Sobel filtering, were utilized on the datasets, and Convolutional Neural Networks (CNN) were used for feature extraction. Furthermore, machine learning algorithms were applied for classification, forming a Hybrid Convolutional Machine Learning algorithm. In this method, the model, which is fed through two different channels compared to conventional learning algorithms, combines classical machine learning classifiers with the CNN model. In the scope of this study, three tasks were outlined. The first two focused on implementing various machine learning algorithms for each dataset, while the third involved merging datasets and employing the Stacking Ensemble Classifier (SEC). For evaluating the models, accuracy and F1-score metrics were used. The results indicate that the highest accuracy rate was achieved in the third experiment, with a score of 98.94%. Additionally, it is also observed that increasing the amount of test data (the difference between 20% Test and 50% Test) has a minimal effect in reducing the model's accuracy metric compared to previous studies.

Emerging as one of the most contemporary machine learning techniques, deep learning has shown success in areas such as image classification, speech recognition, and even playing games through the use of hierarchical architecture which includes many layers of non-linear information. In this paper, a powerful deep learning pipeline, intelligent deep learning (iDeepLe) is proposed for both regression and classification tasks. iDeepLe is written in Python with the help of various API libraries such as Keras, TensorFlow, and Scikit-Learn. The core idea of the pipeline is inspired by the sequential modeling with considering numerous layers of neurons to build the deep architecture. Each layer in the sequential deep model can perform independently as a module with minimum finitudes and does not limit the performance of the other layers. iDeepLe has the ability of employing grid search, random search, and Bayesian optimization to tune the most significant predictor input variables and hyper-parameters in the deep model via adaptive learning rate optimization algorithms for both accuracy and complexity, while simultaneously solving the unknown parameters of the regression or the classification model. The parallel pipeline of iDeepLe has the capacity to handle big data problems using Apache Spark, Apache Arrow, High Performance Computing (HPC) and GPU-enabled machines as well. In this paper, to show the importance of the optimization in deep learning, an exhaustive study of the impact of hyper-parameters in a simple and a deep model using optimization algorithms with adaptive learning rate was carried out.

Finger vein recognition has emerged as a reliable biometric authentication technology combining low forgery risk, high accuracy and low cost. However, challenges inherent to computer vision applications, such as inconsistent image quality and lighting condition variations persist, leading to the adoption of deep learning approaches for improved results. This study explores the use of Simple Siamese Networks to address the issue of reliance on inter-class comparisons, which can introduce bias and increase training complexity due to the need for careful sampling strategies. These issues are inherent in both traditional Siamese networks (which require negative or hard negative sampling) and classification based methods (which rely on the definition of decision boundaries). Originally developed for unsupervised learning, Simple Siamese Networks exclusively utilize positive image pairs to generate embedding representations that cluster images of the same class. In this study, augmented images from multiple shots of the same individual's finger are used as positive pairs, allowing the model to capture intra-class variability and generalize well without relying on inter-class comparisons. Extensive experiments were conducted on two publicly available datasets (FVUSM, MMCBNU), following a cross-validation protocol using three well-known Convolutional Neural Network (CNN) encoders: ResNet18, DenseNet121 and EfficientNet-B0. The proposed method achieves notably low Equal Error Rates (EER), with results as low as 0.056% on the FVUSM dataset. Our findings show that Simple Siamese Networks offer a robust, easier-to-tune and highly reliable alternative to existing approaches for finger vein recognition, eliminating the need for hard negative mining and class-based contrastive learning.