Abstract:This paper discusses a comprehensive review of the previous research in the field of the finger vein recognition system with a focus on finger vein enhancements and features extraction advances and shortcomings. It starts with a general introduction of the biometric system followed by detailed descriptions on finger vein identification, and its architecture archival of it, which includes image acquisition, preprocessing of the image, feature extraction, and vein matching. This study focuses on related work pro… Show more
“…2 Despite advancements in clinical testing, laboratory diagnostics continue to come across numerous challenges in the preanalytical phase, including ordering, acquiring, and holding biological specimens. 3 Pre-analytical errors have consistently been reported as the prominent proportion of errors in earlier studies. These errors, frequently occurring throughout the collection of the specimen by phlebotomists, compromise the rectitude of the biological samples and result in inaccurate diagnostic information.…”
Section: Introductionmentioning
confidence: 68%
“…Enhancement for low-contrast images is calculated by given Equation (3). The purpose of this equation is to adjust the pixel values in a manner that accentuates lowcontrast regions, thereby improving vein discernibility.…”
“…To ensure patient comfort in pathology wards, the development of an error‐free pre‐analytical phase is crucial for the effective treatment of contagion 2 . Despite advancements in clinical testing, laboratory diagnostics continue to come across numerous challenges in the pre‐analytical phase, including ordering, acquiring, and holding biological specimens 3 . Pre‐analytical errors have consistently been reported as the prominent proportion of errors in earlier studies.…”
The intravenous (IV) injection procedure can be a challenging task, especially for individuals with thin veins, obesity, or patients with damaged and pigmented skin. Therefore, the IV procedure necessitates a portable medical device that can be used for academic demonstrations to train medical students or by health care professionals to perform venipuncture. Vein visualization with a vein detector is principally based on the interaction of blood components with wavelengths of the electromagnetic spectrum (EMS). In this paper, we first present the process of image formation in the spectral band of the near‐infrared region (NIR) of EMS. Then, we introduce the image acquisition system with image processing to extract the veins as a noninvasive vein detection method. A Raspberry Pi (Model 4B), along with a night vision camera, serves as an image acquisition tool to capture skin area illuminated by NIR. Following this, the data is transferred to the laptop where it can be filtered and processed using Python image processing tools before being viewed on the monitor. The results achieved through the device are quite encouraging, as the image recognition between veins and adjacent tissues from the skin sample can be clearly marked. The functionality, accuracy, and simplicity associated with this vein detection system make it a potential device for IV placement and the morphological study of disease detection.
“…2 Despite advancements in clinical testing, laboratory diagnostics continue to come across numerous challenges in the preanalytical phase, including ordering, acquiring, and holding biological specimens. 3 Pre-analytical errors have consistently been reported as the prominent proportion of errors in earlier studies. These errors, frequently occurring throughout the collection of the specimen by phlebotomists, compromise the rectitude of the biological samples and result in inaccurate diagnostic information.…”
Section: Introductionmentioning
confidence: 68%
“…Enhancement for low-contrast images is calculated by given Equation (3). The purpose of this equation is to adjust the pixel values in a manner that accentuates lowcontrast regions, thereby improving vein discernibility.…”
“…To ensure patient comfort in pathology wards, the development of an error‐free pre‐analytical phase is crucial for the effective treatment of contagion 2 . Despite advancements in clinical testing, laboratory diagnostics continue to come across numerous challenges in the pre‐analytical phase, including ordering, acquiring, and holding biological specimens 3 . Pre‐analytical errors have consistently been reported as the prominent proportion of errors in earlier studies.…”
The intravenous (IV) injection procedure can be a challenging task, especially for individuals with thin veins, obesity, or patients with damaged and pigmented skin. Therefore, the IV procedure necessitates a portable medical device that can be used for academic demonstrations to train medical students or by health care professionals to perform venipuncture. Vein visualization with a vein detector is principally based on the interaction of blood components with wavelengths of the electromagnetic spectrum (EMS). In this paper, we first present the process of image formation in the spectral band of the near‐infrared region (NIR) of EMS. Then, we introduce the image acquisition system with image processing to extract the veins as a noninvasive vein detection method. A Raspberry Pi (Model 4B), along with a night vision camera, serves as an image acquisition tool to capture skin area illuminated by NIR. Following this, the data is transferred to the laptop where it can be filtered and processed using Python image processing tools before being viewed on the monitor. The results achieved through the device are quite encouraging, as the image recognition between veins and adjacent tissues from the skin sample can be clearly marked. The functionality, accuracy, and simplicity associated with this vein detection system make it a potential device for IV placement and the morphological study of disease detection.
“…Asymmetric keys form the foundation of Public Key Infrastructure (PKI), an encryption approach that needs two keys, one (public key) used in encrypting the plaintext and another (private key) used in decrypting the cyphertext [10], as shown in figure 2 below. This means that none of the keys can perform both functions.…”
Section: Common Encryption Algorithms Used In Cloud Storagementioning
confidence: 99%
“…This means that none of the keys can perform both functions. A public key is available to an individual in need of encrypting a particular information [10]. To decrypt the same information, the user should use the private key.…”
Section: Common Encryption Algorithms Used In Cloud Storagementioning
Among the critical aspects directly linked to technology evolvement is cloud computing. Over the years, the amount of data among people and businesses has been growing, posing a challenge to its storage and management. To help address this issue, tech professionals and companies have attempted to find a secure, reliable, trusted, and user-friendly solution to data storage and management, among such solutions being cloud computing. Storing data in the cloud promotes on-demand services and intense applications boosted by a secured system of configurable computing resources. However, before a user decides to outsource personal data to the cloud, it must be protected by integrating distinct data encryption techniques. Data encryption is essential in advancing the security of any data that users store in cloud storage. Data encryption is classified into symmetric encryption and asymmetric encryption.
The evolution of deep learning has promoted the performance of finger vein verification systems, but also brings some new issues to be resolved, including high computational burden, massive training sample demand, as well as the adaptability and generalization to various image acquisition equipment, etc. In this paper, we propose a novel and lightweight network architecture for finger vein verification, which was constructed based on a Siamese framework and embedded with a pair of eight-layer tiny ResNets as the backbone branch network. Therefore, it can maintain good verification accuracy under the circumstance of a small-scale training set. Moreover, to further reduce the number of parameters, Gabor orientation filters (GoFs ) were introduced to modulate the conventional convolutional kernels, so that fewer convolutional kernels were required in the subsequent Gabor modulation, and multi-scale and orientation-insensitive kernels can be obtained simultaneously. The proposed Siamese network framework (Siamese Gabor residual network (SGRN)) embeds two parameter-sharing Gabor residual subnetworks (GRNs) for contrastive learning; the inputs are paired image samples (a reference image with a positive/negative image), and the outputs are the probabilities for accepting or rejecting. The subject-independent experiments were performed on two benchmark finger vein datasets, and the experimental results revealed that the proposed SGRN model can enhance inter-class discrepancy and intra-class similarity. Compared with some existing deep network models that have been applied to finger vein verification, our proposed SGRN achieved an ACC of 99.74% and an EER of 0.50% on the FV-USM dataset and an ACC of 99.55% and an EER of 0.52% on the MMCBNU_6000 dataset. In addition, the SGRN has smaller model parameters with only 0.21 ×106 Params and 1.92 ×106 FLOPs, outperforming some state-of-the-art FV verification models; therefore, it better facilitates the application of real-time finger vein verification.
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