Based Medical Systems for Patient's Authentication: Towards a New Verification Secure Framework Using CIA Standard.

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.

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.

We present a new architecture of Advanced Encryption Standard (AES) cryptographic circuit which can be used as cryptographic primitives supporting privacy and authentication for radio frequency identification (RFID). RFID is a technology to identify goods or person containing the tags. While it is a convenient way to track items, it also provides chances to track people and their activities through their belongings. For these reasons, privacy and authentication are a major concern with RFID system and many solutions have been proposed. M. Feldhofer, S. Dominikus, and J. Wolkerstorfer introduced the Interleaved Protocol which serves as a means of authenticating RFID tag to reader devices in M. Feldhofer et al., 2004. They designed very small AES hardware circuit as a cryptographic primitive. The proposed circuit requires about 1,000 clock cycles to encrypt a 128-bit block of data. In this contribution, we introduce a novel method to increase the operating speed of previous method for low-cost AES cryptographic circuits. Our low-cost AES cryptographic circuit can encrypt 128-bit data block within 870 clock cycles using less than 4000 gates on a 0.25 mum CMOS process

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.

Smartwatches like Fitbits provide users with easy access to quantifiable health data. In the sports industry, tracking this biometric information may be particularly beneficial to athletes, whose livelihoods revolve around their health and fitness. Nonetheless, under the current regime, professional and collegiate athletes’ biometric health data are inadequately protected. Data privacy law is still in its infancy, but in the meantime, athletes must consider that motivations to sell or misuse players’ biometric information may outpace legal developments. This Paper will analyze the promise and risk of collecting professional and collegiate athletes’ health and biometric data, particularly through fitness wearables. It will provide a closer look at wearables in professional sports and consider the increased risk posed to college athletes. Finally, this Paper will consider possible solutions to maximize the benefits of newfound technology while simultaneously minimizing risks to players’ health information, privacy, and personal data ownership.

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.

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 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.

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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At present, the finger vein has attracted more attention, but the existing finger vein extraction systems are single finger systems, multi-finger case is not yet to come, and few work was did about the research of corresponding verification algorithms. This paper proposes a two modality-based bi-finger vein verification system. Both the finger vein and finger shape could be extracted from the single image acquired from the sensor. The system includes the new finger vein network extraction algorithm. The intersection of the forefinger and middle finger as the origin is introduced to the coordinate system, proposing a method including determining ROI, the finger vein and shape features extraction and its corresponding fusion verification. The experiment shows that the new finger vein network extraction algorithm can extract the finger vein network well. The introduction of the coordinate system effectively solves the problem that not easy to determine ROI, and the false accept rate of bi-pattern fusion verification system is much smaller than the false accept rate of single-feature fusion verification system.

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.

IoT technology is growing very fast and one of the requirements of this technology is integrating different communications protocols and networks. In an IoT network, security of such a heterogeneous and large network is very important. Transport systems are part of this super network and in-vehicle protocols are used in such systems. Unfortunately, the Controller Area Network (CAN) protocol, the most popular protocol in the systems, designed without security in mind. In this paper, the Advanced Encryption Standard (AES), an encryption algorithm, is used to prevent masquerade and replay attacks in order to secure CAN protocol to an appropriate level. The paper has a plan to explore for an efficient implementation of AES encryption algorithm for the communication protocol. These implementations have been evaluated on an FPGA ML605 development board. The best implementation of the AES among 8-, 16-, 32- and 64-bit data paths has been investigated. The most important criteria for the protocol in these AES designs are such as consumed power, area, and cost in addition to providing better throughput. The 64-bit structure of the designed AES is selected which has the frequency of 21.4 MHz, significant throughput of 412.39 Mbps, reasonable area of 784 slice on Spartan III FPGA.

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.