Question:
Discuss About The Biometrics Authentication Present Future?
Cybersecurity is emerging as a key sector in this connected world, in a global network where data is constantly flowing and stored for endless durations. The inexorable growth of threats on traditional security systems has pushed systems to manipulate sensitive data to bet on the promises of biometrics (Dasarathy & Sullivan, 2014).
Biometrics being the set of computer techniques automatically recognizes an individual based on his or her physical, biological and even behavioural characteristics. It differentiates on the basis of the recognition of iris, voice, face or fingerprints and thus eliminates the need for the customer to remember combinations of passwords and username. At the same time, these technologies provide more robust protection against fraud and cyber-attacks. Biometric data permanently change the relationship between the body and identity because they make the physical features “machine-readable” and subject for later use. Biometric facts can be stored and treated in several ways. The biometric information of a person are sometimes stored and processed in the raw state, allowing the recognition of their source without the necessity of having knowledge for example, photographing a face, pattern of the iris, photographing a fingerprint or recording the voice. In other cases, raw biometric info is processed so that only certain qualities are extracted and kept under biometric model form (Yang & Zhou, 2016). Biometric data can come from different sources, which cover the physical, physiological, behavioural or psychological aspects of a person.
The earliest known biometric verification method is the fingerprint. The imprint of the thumb on seals of clay was already used as a means of identification unique in ancient China. Modern biometric authentication has become practically instantaneous. It is also increasingly precise, thanks to the advent of computerized databases and the digitization of analog data.
As a consequence of recent technological developments, it is now likely to use biometrics for categorization / segregation. Generally, it involves procedures such as registration, storage and mapping. – The presence of biometric data encompasses all procedures that take place biometric system in order to bring out the relevant data from a biometric source and to link that data to an identity. Necessary data during the registration phase must be sufficient to identification, authentication, categorization or verification (Al-Hudhud & Alzamel, 2014).
The identification of a person through a system biometric method generally involves comparing a person’s biometric data (acquired at the time of identification) with several biometric models stored in a database (in other words, a “one-to-many” comparison process). Biometric verification / authentication: Verification of a person by a system biometric method generally involves comparing a person’s biometric data (acquired at the time of the verification) and a single biometric model stored in one (i.e. a “one-to-one” comparison process).
For a long time, primarily government authorities used biometric technologies, but recently the situation has gradually changed. Today, commercial organizations play a chief role in the usage of these tools and the development of innovative products. One of the main drivers of this situation is that the technology has evolved in such a way that biometric systems that previously only worked well in conditions controlled have been perfected and now suitable for widespread use in a range of different environments (Kawamata & Ishii, 2016). In this way, biometrics replaces or improves in some cases, the conventional identification methods, especially those based on several identifying factors required for authentication systems solid. Biometric technologies are also increasingly used in applications that can quickly and easily identify a person but with a low accuracy. The use of biometric technology also extends gradually beyond their field of initial application: from identification and authentication analysis behavior through monitoring and fraud prevention. Advances in networking and computer technologies also lead to the rise of what is considered the second generation of biometric systems, based on the use of behavioral and psychological features, alone or in combination other conventional systems to form multimodal systems (Zakariah & Khan, 2017).
Multimodal Biometry is the association of different technologies to improve the accuracy. Biometric systems use at least two biometric features of the same person during the matches. These systems can work in different ways, either by collecting different biometric data with different sensors either by collecting several units of the same biometric data. Some studies also include this category system that perform multiple readings of the same biometric data and systems that use multiple algorithms to extract strokes from the same sample (Paul & Irvine, 2016).
New trends of biometrics technologies can be considered mature and are found in various commercial applications, e-government and application of the law. The fingerprints, hand geometry, iris scanning and certain types of facial recognition, among others, are among these technologies.
Biometric systems are increasingly used by both public and private bodies; in general, in the public sector, law enforcement agencies regularly use biometrics; in the financial, banking and e-health and in other sectors, such as education, retail and telecommunications. It was recognized that biometric systems have, from the first moment of their application, the potential to raise serious concerns in various areas, including privacy and data protection, which certainly influenced their acceptance social and fueled the debate on the legality and limitations of their use and guarantees to mitigate identified risks including the possibility of disguise collection, storage and processing equipment as well as the collection containing highly sensitive information that can invade the most intimate space of the person. The traditional reluctance of biometric systems has been linked to the protection of rights individual, and it still is (Kremic & Subasi, 2015).
When biometric systems are used, it is difficult to obtain results 100% error free. Perhaps the reason is to look for differences environment when acquiring data (lighting, temperature, etc.) and in differences in the equipment used (cameras, scanners, etc.). The evaluation parameters of most frequently used are the false acceptance rate (TFA) and the rate of (TFR), which can be adapted according to the system used: – the rate of false acceptances (TFA) is the probability that a biometric system incorrectly identifies a person or fails to reject an impostor. It measures the percentage of invalid inputs that are wrongly accepted.
The accuracy and security of the registration process is essential to the functioning of the whole system. A person can re-register in a biometric system to update biometric data recorded. – Storage of biometric data: the data collected during phase can be stored locally in the operation center where registration has taken place (e.g., in a reader) for later use or on a device (e.g., on a smart card) or may be sent or stored in a centralized database accessible by one or more several biometric systems (Abo-Zahhad & Ahmed, 2014).
Conclusion
Biometric systems are closely related to a person because they can use a trait unique for a person for identification or authentication purposes. Used effectively and successfully in scientific research, biometric data are essential to forensic science and are a valuable part of access control.
They contribute to increasing the level of security, facilitating and speeding up identification and authentication procedures and make them timely. Previously, the use of this technology was costly and therefore had limited impact on the rights of individuals to the protection of their data. During the last years, this situation has radically changed. Thanks to technological advances, the power of computers and storage space is cheaper, which has led to the appearance of digital identity and billions of photographs on social networks (Zafeiriou & Zhang, 2015).
Fingerprint operated machines and CCTV devices have become inexpensive gadgets. The development of these technologies has facilitated a number of operations, contributed to many crimes and increased the reliability of access control systems, but also created new threats to fundamental rights. Identity theft is no longer a theoretical threat. If other new technologies targeting large populations recently rose concerns about data protection do not focus not necessarily on the establishment of a direct link with a particular person, or the creation of this link requires considerable effort, the biometric data are, per se, directly connected to a person. This is not always an advantage, it even implies several disadvantages. For example, equipping CCTV systems and smart phones with facial recognition systems using social networks could put an end to the anonymity and untraced people. These technical innovations, which are often presented as technologies that only improve the user interface and the practical side of the applications, could lead to a progressive loss of privacy if adequate safeguards are not implemented. Consequently, this opinion identifies technical and organizational measures aimed at mitigate risks to privacy and data protection contributing to preventing the negative impact on the privacy of humans and on the fundamental rights of the latter to the protection of personal data.
Today, biometric data are used to grant or deny access to premises in a company, identity documents, future remote banks, and of course the smartphones and other connected devices. Consequently, the consequences can be catastrophic in case of theft. Thus one needs to be careful about the applications that under the pretext of security ask about identification via a smartphone hosting the given data.
References
Abo-Zahhad, M., Ahmed, S. M., & Abbas, S. N. (2014). Biometric authentication based on PCG and ECG signals: present status and future directions. Signal, Image and Video Processing, 8(4), 739-751.
Al-Hudhud, G., Alzamel, M. A., Alattas, E., & Alwabil, A. (2014). Using brain signals patterns for biometric identity verification systems. Computers in Human Behavior, 31, 224-229.
Dasarathy, B., Sullivan, K., Schmidt, D. C., Fisher, D. H., & Porter, A. (2014, May). The past, present, and future of MOOCs and their relevance to software engineering. In Proceedings of the on Future of Software Engineering (pp. 212-224). ACM.
Kremic, E., & Subasi, A. (2015). Significance Précis of Artificial Authentication Model via Mobile.
Paul, G., & Irvine, J. (2016). IEDs on the Road to Fingerprint Authentication: Biometrics have vulnerabilities that PINs and passwords don’t. IEEE Consumer Electronics Magazine, 5(2), 79-86.
Kawamata, T., Ishii, T., & Akakura, T. (2016, December). Face authentication for e-Learning using time series information. In Teaching, Assessment, and Learning for Engineering (TALE), 2016 IEEE International Conference on (pp. 116-121). IEEE.
Yang, B., Zhou, C., Wang, S., Fan, X., & Li, C. (2016, October). Facial skin color measurement based on camera colorimetric characterization. In SPIE/COS Photonics Asia (pp. 1002218-1002218). International Society for Optics and Photonics.
Zafeiriou, S., Zhang, C., & Zhang, Z. (2015). A survey on face detection in the wild: past, present and future. Computer Vision and Image Understanding, 138, 1-24.
Zakariah, M., Khan, M. K., & Malik, H. (2017). Digital multimedia audio forensics: past, present and future. Multimedia Tools and Applications, 1-32.
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