Biometric access control involves using biological, physiological, or behavioral characteristics of individuals for identification and authentication to restrict access to locations where valuable information and vital assets are stored, for instance, a data center (Kennedy Okokpujie et al., 2021). Biometric recognition systems are difficult to bypass compared to a traditional token and password-based systems (Pandya et al., 2018). The most common types of biometrics used include retina scans, fingerprint scans, facial recognition, voice recognition, amongst others. Implementing a biometric access control system in a sensitive area such as a data center relies heavily on scanning and verifying the unique biological characteristics of the individual requesting access to those stored in the biometric database. Though most biometric technologies are implemented with the primary objective of managing access to facilities, they can also be incorporated into server cabinets as a physical security measure (Kennedy Okokpujie et al., 2021).
Implementing Biometric Access Control in a Data Center
Implementing biometric access solutions in a data center may require one or more forms of biometric identification and authentication or the deployment of different scanning methods at different security levels to provide a robust and diversified security measure. The use of two or three-factor authentication is an effective strategy to verify an individual’s identity. Thus, incorporating something you know, such as a PIN with something you are, such as a fingerprint or retinal scan, provides sufficient access control restrictions to a data center. The fingerprint identification system is the most widely used form of biometric traits due to its robustness against spoofing attacks and the reasonable cost of implementation (Pandya et al., 2018). Fingerprinting identification involves using infrared sensors to capture finger-veins that are embedded inside a finger since no two fingers have identical fingerprints (ridges, grooves, and direction of lines). Fingerprint unique qualities are determined by ridge and minutiae features such as bifurcation and spots, the ridge endings. The steps to recognize a fingerprint involves; fingerprint image enhancement, minutiae-based fingerprint extraction, fingerprint matching.
Another method of administering biometric access control in a data center is using an Iris scanner. The Iris technology combines iris pattern recognition with computer vision, optics, and statistical inference to identify and authenticate an individual (Kennedy Okokpujie et al., 2021). Iris biometric scan recognizes a person by examining the irregular pattern of the iris, given that irises are unique and structurally distinctive. The steps involved in Iris recognition include; pupil detection. Iris detection, normalization, feature extraction, and matching.
In designing a biometric access control system for a data center, a combination of fingerprint and iris biometric solutions are a great choice. Fingerprinting is easy and less costly to implement when compared to the Iris scanner. Also, fingerprinting technologies are vastly used in all industries. Thus, there is a rapid development of different fingerprint Application Programming Interfaces (APIs) that facilitate the implementation of fingerprint biometric access control systems on both legacy systems and new systems and mobile devices (Huh & Seo, 2019). However, some fingerprint biometric readers may not be sufficient to handle a significant variation in populations, and search results may sometimes return multiple matches. For this reason, a backup biometric technology such as Iris recognition technology may be used to resolve the problem of multiple matches. Even though implementing an Iris biometric access control technology is more expensive and cumbersome, Iris recognition has proven to be a non-invasive biometric technology with a very high accuracy rate when compared to fingerprinting (Mo & Chen, 2021).
References
Huh, J.-H., & Seo, K. (2019). Blockchain-based mobile fingerprint verification and automatic log-in platform for future computing [Article]. Journal of Supercomputing, 75(6), 3123-3139. https://doi.org/10.1007/s11227-018-2496-1
Kennedy Okokpujie, S. A., Abayomi-Alli, O., John, A., Adoghe, A., & Okokpujie, I. (2021). Implementation of a bimodal biometric access control system for data center. International Journal of Advanced Research in Engineering and Technology (IJARET), 12(3). https://doi.org/10.34218/IJARET.12.3.2021.038
Mo, X., & Chen, T. (2021). Research on image preprocessing for iris recognition. Journal of Physics: Conference Series, Volume 2031, 2021 2nd International Conference on Signal Processing and Computer Science (SPCS 2021) 20-22 August 2021, Qingdao, China,
Pandya, B., Cosma, G., Alani, A. A., Taherkhani, A., Bharadi, V., & McGinnity, T. M. (2018, 25-27 May 2018). Fingerprint classification using a deep convolutional neural network. 2018 4th International Conference on Information Management (ICIM),
