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Implicit Ontology Changes Driven by Evolution of e-Health IoT Sensor Data in the τOWL Semantic Framework
Published in Om Prakash Jena, Bharat Bhushan, Nitin Rakesh, Parma Nand Astya, Yousef Farhaoui, Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems, 2022
Zouhaier Brahmia, Fabio Grandi, Abir Zekri, Rafik Bouaziz
Sharma et al. [40] study the role of wireless sensor networks and biometric-based models (like two-factor remote authentication, and user verification and authorization using fingerprint biometrics) in healthcare systems. In particular, the authors have presented a comparative table that provides advantages and disadvantages of several biometric-based models applied in healthcare. Notice that such models are efficient tools for controlling access to electronic health records, securing patient data, and therefore protecting both patient data confidentiality and privacy. Biometrics is the science of analyzing physical characteristics (e.g., fingerprints, eye iris and retina patterns, shape of the hand, finger, or face) or behavioral characteristics (e.g., speech recognition, signature dynamics) of each individual and enabling the authentication of his/her identity.
Survey of Biometric Tools and Big Data
Published in Rodgers Waymond, Artificial Intelligence in a Throughput Model, 2020
Different type of behavioral biometrics includes voice recognition, gait recognition, dynamic signature and keystroke dynamics. Behavioral identifiers are typically being implemented in conjunction with another method due to its lower reliability. Nevertheless, as technology improves, these behavioral identifiers are increasing in status. Unlike physical identifiers, which are restricted to a particular fixed set of individual characteristics, the only limits to behavioral identifiers is the human imagination. For example, this approach is often utilized to distinguish between a human and a robot. This process can aid an organization to filter out spam or detect attempts to brute-force a login and password. As technology advances, the systems are likely to improve at precisely identifying individuals, but less effective at distinguishing between humans and robots.
A Survey of Artificial Intelligence Techniques Used in Biometric Systems
Published in Chiranji Lal Chowdhary, Intelligent Systems, 2019
C. N. Patill, E. Naresh, B. P. Vijay Kumar, Prashanth Kambli
Nowadays biometrics is one of the most trending technologies in various fields, used for security and privacy purpose. Biometrics is used for the recognition and authentication purpose of an individual and for the security and privacy purpose to secure the data access by the unauthorized person or entity. Fingerprints, facial recognition, iris biometrics, and retina biometrics are some of the physical biometric identification, recognition, and authentication methods. Because of the unique signature of an individual, the unauthorized person or entity cannot access the device or other personal information of an individual. Artificial intelligence technique is used to provide the best feature or characteristic part of the individual for the identification and recognition purpose and to reduce the system complexity. Biometric system works with peculiar characteristics feature part of the human body. Because of the high quality of clarity of image, it acquires a very large amount of space and then gradually it reduces the efficiency of the biometric system. So, artificial intelligence is used to provide efficient and proper identification of the individual using peculiar characteristics. Human face recognition plays a very important role in biometric applications that are used for photography, human–computer interaction, artificial intelligence, to unlock the mobile devices and in various security applications.19–24
Deep learning framework for biometric authentication using retinal images
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Jarina B. Mazumdar, S. R. Nirmala
Biometric authentication is extensively used in banking, forensics and several security applications such as access control, identification and surveillance. The retinal images are the most reliable biometric traits and are unique for each individual even for identical twins (Tower 1955). The blood vessel patterns of retina remain stable with time and no clinical expert can change this structure. Retina is situated at rear end of the eye, so it is very difficult to forge (Jain et al. 2004). Hence, the authentication system based on retinal images is reliable and accurate for person identification. Even though the retina possesses such enticing features in comparison to other biometric traits, it is rarely used. Because the image acquisition procedure is considered invasive and needs a comparatively high degree of user cooperation (e.g. eye glasses need to be removed) (Ahmed et al. 2014). Many high-definition retinal scanners are designed to improve the quality of acquired images, and with a reduced amount of acquisition time. This leads to generation of huge amount of data every day. Thus, more efficient tool for data analysis is required to carry out the authentication task. The retinal images that have been acquired may also be adversely affected by geometric transforms like rotation, translation and also a minor scaling due to the movement of the eye or head placing in front the scanner. So, the person authentication based on retinal images must be invariant to such deformities.
Casualty Identification with Dental Radiographs and Photographs
Published in IETE Journal of Research, 2023
B. Vijayakumari, M. Vasanthal, S. Dhivya Dharshini
People travel widely these days and the chance for them, to meet an accident is also potentially high. Casualty is a person who got injured or killed during an accident. Biometric is a metric related to the physical or behavioural characteristics of a human being. A biometric system is used to identify and authenticate a person based on the information, which are unique biological traits. There are various physiological biometric measurements such a fingerprint, face, hand, Iris, and DNA matching and also behavioural biometric measurements such as signature dynamics, keystroke dynamics, voice recognition, etc. Physiological measurements offer more stability throughout the lifetime of the individual (i.e.) they are not subjected to any level of stress, contrary to the identification of behavioural measurement. After an accident, the chance for damage to the physiological biometric measurement of the casualty, such as their fingerprint, face, and the iris, is high. So, the Dental Biometric has been chosen here for casualty identification. In Dental Biometric, the information from the dental structure of an individual is used to identify the casualty. It is now a revolutionized field in forensics that continues to aid in the identification of unknown human remains, which earned great fame.
Biometrics-Based Mobile User Authentication for the Elderly: Accessibility, Performance, and Method Design
Published in International Journal of Human–Computer Interaction, 2022
Kanlun Wang, Lina Zhou, Dongsong Zhang
Physiological-based biometrics tend to be secure and difficult to be stolen and forged but raise privacy concerns. Particularly, fingerprint recognition (e.g., Blanco-Gonzalo et al., 2015; Iqbal et al., 2020; Zheng et al., 2020) and face recognition (e.g., Corsetti et al., 2019; Shien & Singh, 2017; Wu & Wang, 2019) are the most prevalent physiological-based biometrics in use and have proven to be the most easy-to-use methods. Both are free of hearing and cognition requirements. Although fingerprint-based MUA also supports the elderly with vision impairments, it faces usability [e.g., those elderly users who have dry skin and skin tears (White-Chu & Reddy, 2011)] and security challenges [i.e., bypassed by using fingerprint residue or a gummy fingerprint (Matsumoto et al., 2002)]. Face recognition-based MUA also has its own vulnerabilities, such as presentation attacks [e.g., photo, video, and 3D mask attacks (Mohammadi et al., 2018)] and sensitivity to the background lighting (Wang et al., 2018). Additionally, the facial expressions of the elderly are limited (Corsetti et al., 2019).