INNOVATIVE USE OF EMERGING BIOMETRIC TECHNOLOGY IN ENHANCING AIRPORT SECURITY AT JOMO KENYATTA INTERNATIONAL AIRPORT IN NAIROBI, KENYA
Keywords:
unauthorized access, airport security, Aviation, Biometric Recognition, Identification
Abstract
Airports are becoming increasingly vulnerable to impersonation and unauthorized access to designated areas by various cadres of employees due to the diversity of numbers and roles of airport employees. Unauthorized individuals can approach an aircraft or gain access to the airside by exploiting flaws in airport access control methods. Airport employee entry points are perhaps the weakest and most complex to access control. The study's problem is that many authorities have long attempted to improve security standards in the aviation industry through various means. While maintaining a secure aviation industry has always been a top priority, there has been a renewed focus on aviation security and safety since September 11, 2001. More than 77 percent of airports and 71 percent of airline security use biometric technology. In 2018, global biometric systems generated around $21.8 billion in revenue, which was used to advance airlines and airports. As a result, the goal of this study was to explore and implement the utilization of emerging biometric technology as a means to enhance the airport security measures at Jomo Kenyatta International Airport in Nairobi, Kenya. The specific objectives were to assess the effectiveness of biometric face recognition, evaluate the adoption and applications of biometric fingerprint identification system and examine the adoption and applications of automated passport control systems in enhancing Airport security in JKIA, Kenya. The study adopted a descriptive survey research approach with a target population of 1000 airport employees from various departments. Questionnaires were administered to a statistically meaningful sample size of 230 respondents and analyzed using descriptive and inferential statistics. Key informant interviews were also carried out to corroborate the findings from the questionnaires. The study findings reveal that biometric facial recognition technology is considered to have a significant effect on airport security with correlation of 0.569. Further, biometric fingerprint recognition is highly correlated to airport security with a coefficient of 0.541. Automated passport control had a significant effect on airport security with a correlation coefficient of 0.541. Moreover, the findings concluded that adopting both biometric fingerprint and automated passport control technologies would boost airport security. The study recommended that more efforts should be put on facial recognition technology as it is efficient in enhancing airport security. Further, the study recommended that the three technologies should be put together for better results.
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Madara, D. J. A., Okeyo, G., & Kimwele, M. (2017). A Fingerprint &Pin Authentication to Enhance Security At The Automatic Teller Machines.
Maheswari, S. U., & Chandra, E. (2013). An Efficient Fingerprint Denoiser for Fingerprint Recognition. International Journal of Computer Applications, 66(22).
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Negri, N. A. R., Borille, G. M. R., & Falcão, V. A. (2019). Acceptance of biometric technology in airport check-in. Journal of Air Transport Management, 81, 101720.
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Patel, H., & Asrodia, P. (2012). Employee Attendance Management System Using Fingerprint Recognition. International Journal of Electrical, Electronics and Computer Engineering, 1(1), 37-40.
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Plonsky, L., & Derrick, D. J. (2016). A meta‐analysis of reliability coefficients in second language research. The Modern Language Journal, 100(2), 538-553.
Putra, B. H., & Arifin, R. (2020). The Adoption Of Border Technology Of Immigration Control And Autogates In Indonesia. Sintech (Science And Information Technology) Journal, 3(2), 137-148.
Ramos, A. P., Gustafsson, O., Labert, N., Salecker, I., Nilsson, D. E., &Averof, M. (2019). Analysis of the genetically tractable crustacean Parhyalehawaiensis reveals the organisation of a sensory system for low-resolution vision. BMC biology, 17(1), 1-19.
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