Biometric Authentication Model Based on Transformation of Face Image into a PIN Number Usable During the Covid-19 Pandemic

Abstract

The digitization trend is developing throughout the crisis caused by the COVID-19 pandemic. The volume of digital payments is increasing. The most common way of checking the authentication in electronic payment systems is the PIN number that users type into the PINPAD device. Digital payment devices still require the entry of smart cards and the manual entry of a PIN into an ATM or POS device. In order to reduce the possibility of infection due to contamination from multiple touches of the PINPAD device by different people, cardholders use the PINPAD device with gloves. Instead of entering the PIN number on the PINPAD device, biometric authentication is available for the authentication process. On the other hand, the use of gloves and a medical face mask, during the Covid-19 pandemic, limits the biometric scanning of fingerprints and facial images. In this paper, a biometric authentication model is proposed that uses biometric features of the eyes, as these biometric data are available to scanners even in the case when the cardholder uses a protective medical mask on his/her face. The proposed model transforms data on the basis of correlations of characteristic points around the eyes and eye color into a stable PIN. Quantitative presentation of the experimental results confirms that for six different facial expressions of each of the 50 tested persons, the deviation of the authentication PIN from the reference PIN does not exceed 1%. Using the proposed innovative model, the existing infrastructure of payment systems should not be changed.