Finger Vein Biometrics: Taxonomy Analysis, Open Challenges, Future Directions, and Recommended Solution for Decentralised Network Architectures

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

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.
 
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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.

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.

Objective: This research aims to survey the efforts of researchers in response to the new and disruptive technology of skin cancer apps, map the research landscape from the literature onto coherent taxonomy, and determine the basic characteristics of this emerging field. In addition, this research looks at the motivation behind using Smartphone apps in the diagnosis of skin cancer and in health care and the open challenges that impede the utility of this technology. This study offers valuable recommendations to improve the acceptance and use of medical apps in the literature. Methods: We conducted a comprehensive survey using the keywords “skin cancer,” “apps,” and “Smartphone” or “m-Health” in different variations to find all the relevant articles in three major databases: Web of Science, Science Direct, and IEEE Xplore. These databases broadly cover medical and technical literature. Results: We found 110 articles after a comprehensive survey of the literature. Out of the 110 articles, 46 present actual attempts to develop and design medical apps or share certain experiences of doing so. Twenty-eight articles consist of analytical studies on the incidence of skin cancer, the classification of malignant cancer or benign cancer, and the methods of prevention and diagnosis. Twenty-two articles comprise studies that range from the evaluative or comparative study of apps to the exploration of the desired features for skin cancer detection. Fourteen articles consist of reviews and surveys that refer to actual apps or the literature to describe medical apps for a specific specialty, disease, or skin cancer and provide a general overview of the technology. New research direction: With the exception of the 110 papers reviewed earlier in results section, the new directions of this research were described. In state-of-the-art, no particular study presenting watermarking and stenography approaches for any type of skin cancer images based on Smartphone apps is available. Discussion: Researchers have attempted to develop and improve skin cancer apps in several ways since 2011. However, several areas or aspects require further attention. All the articles, regardless of their research focus, attempt to address the challenges that impede the full utility of skin cancer apps and offer recommendations to mitigate their drawbacks. Conclusions: Research on skin cancer apps is active and efficient. This study contributes to this area of research by providing a detailed review of the available options and problems to allow other researchers and participants to further develop skin cancer apps, and the new directions of this research were described.

The vulnerability of finger vein recognition to spoofing attacks has emerged as a crucial security problem in the recent years mainly due to the high security applications where biometric technology is used. Recent works shown that finger vein biometrics is vulnerable to spoofing attacks, pointing out the importance to investigate counter-measures against this type of fraudulent actions. The goal of the 1st Competition on Counter Measures to Finger Vein Spoofing Attacks is to challenge researchers to create counter-measures that can detect printed attacks effectively. The submitted approaches are evaluated on the Spoofing-Attack Finger Vein Database and the achieved results are presented in this paper.

Finger vein biometric has become most promising recognition method due to its accuracy, reliability and security. This paper discusses a novel technique for finger veins features extraction using Discrete Wavelet Packet Transform (DWPT) based method. The DWPT without HH subband decomposition is applied on ROI of 96×64 size finger veins image up to third level. The average standard deviation and average energy of each decomposition level are used for the creation of features vector database. The Euclidean, City Block and Canberra distance classifiers are used for the classification of finger veins images. The performance of proposed method is evaluated on the standard finger veins image ROI database of SDUMLA Shandong University. Experimental results show that the proposed method gives better results as compare to the standard Discrete Wavelet Transform (DWT) and DWPT Methods.

The vehicle-to-infrastructure (V2I) communication system allows the exchange of information between vehicles and road infrastructures. It aims to avoid or reduce vehicular accidents, increase mobility, and provide other road safety benefits. This paper aimed to review and analyze the literature on data exchanges in the V2I communication system. The factors considered to improve the understanding of various contextual aspects and the characteristics of the field were motivations, open challenges, and recommendations from other researchers. We systematically searched all articles on data exchanges in the V2I communication system from the three main databases, namely ScienceDirect, Web of Science, and IEEE Xplore, from 2008 to 2018. These indices were sufficiently extensive to encompass our field of literature. A total of 70 articles were selected based on our inclusion and exclusion criteria. Most studies (42/70) covered a developed V2I communication system, while numerous articles (22/70) focused on general research on the V2I communication system. The smallest portion of articles (6/70) comprised reviews and surveys. The V2I system plays a key role in vehicular ad hoc networks but is less implemented than vehicle-to-vehicle communication owing to its deployment costs and maintenance requirements. However, numerous studies have been conducted on the V2I communication system to promote its utility. Research areas on V2I communication classification vary but are all equally vital. We expect this systematic review to help emphasize current research opportunities and thus extend and create additional research fields.

In the recent years, finger vein biometrics has been gaining traction in commercial uses. Despite its wide deployment for user authentication, there is still a risk associated with insecure biometric capture process known as presentation attacks where the attacker uses fake finger vein pattern to spoof the finger vein sensor. This raises the need for an efficient method to detect spoofed finger vein images to ensure the security of the system. In this paper, a multi-scale histogram of oriented gradients representation is proposed for presentation attack detection (PAD) with minimal pre-processing step involved. The results are evaluated with a benchmark dataset and compared with the other PAD methods with promising results.

A Multimodal authentication system with fusion at decision level employing Iris and Finger Vein is developed. This paper also proposes a novel feature extraction technique for finger vein and iris traits. This approach combines the decisions from the individual modalities using the conventional AND rule. Experimental results demonstrate the effectiveness of the proposed multimodal authentication system for accurate person authentication.

Traditional identity verification of students based on the human proctoring approach can cause a scam identity verification and ineffective processing time, particularly among vast groups of students. Most student identification cards outdated personal information. Several biometric recognition approaches have been proposed to strengthen students’ identity verification. Most educational adoption technologies struggle with evaluation and validation techniques to ensure that biometric recognition systems are unsuitable for utilization and implementation for student identity verification. This study presents the internet of things to develop flexible biometric recognition systems and an approach to assess the quality of biometric systems for educational use by investigating the effectiveness of identity verification of various biometric recognition technologies compared to the traditional verification method. The unimodal, multimodal, and semi-multimodal biometric technologies were tested using the developed internet of things-base biometric recognition systems examined by applying the proposed quality metrics of scoring factors based on accuracy, error rate, processing time, and cost. Hundreds of undergraduate exam takers were a sample group. Key findings indicate that the designed and presented systems suitably attain identity verification of exam students using a unimodal biometric. The unimodal facial biometric system promises excellent support. A unimodal fingerprint biometric system ensures second excellent aid for student identity verification. However, multimodal and semi-multimodal biometric systems provide better accuracy with fewer handling times and higher costs. This study contributes significantly to the knowledge of utilizing biometric recognition for identity verification in smart educational applications.

Multibiometric systems are very applicable in human identification systems nowadays. However, using different biometrics may need extra acquisition sensors besides algorithm complexity. In this paper, a new multi-instance human identification system is proposed using different finger vein biometrics. In the proposed method, the multibiometric system is implemented using score level fusion. A complete investigation is done on effectiveness of different score level fusion strategies. For the proposed multi finger vein biometric system experimental results confirm that maximum score level fusion strategy outperforms other fusion methods in addition to some classical fusion algorithms in terms of EER.

Recent years have illustrated the significantly pervasive interest in transparent antennas. The number and the popularity of transparent antenna applications have escalated dramatically. Although antenna applications are diverse and available across multiple platforms, some of these antennas are unsuitable for practical usage, particularly in cases associated with renewable energy. As such, this study presents the review of related articles pertaining to transparent antennas across a range of platforms to identify their best practices. The methods applied in prior research work within this domain were identified. Relevant articles published between 2015 and September 2020 were gathered from four major databases: Science Direct, Web of Science (WOS), IEEE Xplore, and Scopus. The identified indicators were considered as broad and reliable to cover this field of literature. The articles (n=81) were selected based on inclusion and exclusion criteria. This paper concentrates on the present views and opportunities to further understand the research segment of transparent antenna.

The use of finger veins as biometric authentication is becoming increasingly popular. However, low-quality finger vein images pose challenges, necessitating innovative approaches for accurate authentication. This research investigates the potential of deep learning techniques in addressing this issue, focusing on two prominent architectures: U-Net and the proposed Sequential Model. The study conducts a comparative analysis of the performance of these models in low-quality finger vein image authentication scenarios. U-Net, known for its image segmentation capabilities, is explored for feature extraction, while the Sequential Model, incorporating a modified VGG16 architecture, brings temporal context through LSTM layers. The research presents an in-depth evaluation of both models based on accuracy, recall, precision, and other relevant metrics. The findings shed light on the suitability of each approach for enhancing the reliability of finger vein authentication in challenging data quality contexts.

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%.