Evaluation of two biometric access control systems using the Susceptible-Infected-Recovered model

Abstract

This paper evaluates the effectiveness of decisions made on two single-mode biometric systems based on facial recognition and fingerprints for access control. To achieve this, we first implemented an embedded system under Arduino to allow us to open and close doors, then we programmed two biometric recognition systems, namely facial recognition and fingerprint recognition, and finally we exploited the Susceptible-Infected-Covered model without demographics to evaluate the efficiency of these two access control systems. The variables used in the analysis were the number of individuals enrolled in the biometric system to be subject to access control (Susceptible), the number of individuals enrolled in the biometric system and denied access by the system, as well as the number of individuals not enrolled in the biometric system and allowed access by the system (Infected), and the number of false acceptance rates and false rejection rates at time t in the systems (Retrieved). In a sample of 600 individuals, of which 300 were enrolled and 300 were not, our two simple modal access control systems each obtained the following results: 270 true positives, 30 false negatives, 48 false positives and 252 true negatives for the facial recognition system, compared to 288 true positives, 12 false negatives, 24 false positives and 276 true negatives for the fingerprint recognition system, which constitute our confusion matrix. Based on this confusion matrix, we were able to exploit the false rejection rates and false acceptance rates to correct for these inconveniences using the SIR model, i.e. 78 infected individuals for the facial recognition system, compared to 36 infected individuals for the fingerprint recognition system over a period of 216 days. The results show that the fingerprint recognition system is more efficient than the facial recognition system, according to the SIR model without demographic formulation.