Identity Verification Using Face Recognition Improved by Managing Check-in Behavior of Event Attendees

Abstract

This is an extension from a selected paper from JSAI2019. This paper proposes an identity-verification system using continuous face recognition improved by managing check-in behavior of event attendees such as facial directions and eye contact (eyes are open or closed). We previously developed Ticket ID system for identifying the purchaser and hold-er of a ticket. This system carries out face recognition after attendant’s check-in using their membership cards. The average face-recognition accuracy was 90%, and the average time for identity verification from check-in to admission was 7 seconds per person. The system was proven effective for preventing illegal resale by verifying attendees of large concerts. The problem with this system is regarding face-recognition accuracy. This can be mitigated by securing clear facial photos because face recognition fails when unclear facial photos are obtained, i.e., when event attendees have their eyes closed, are not looking directly forward, or have their faces covered with hair or items such as facemasks and mufflers. In this paper, we propose a system for securing facial photos of attendees directly facing a camera by leading them to scan their check-in codes on a code-reader placed close to the camera just before executing face recognition. The system also takes two photos of attendees with this one camera after an interval of about 0.5 s to obtain facial photos with their eyes open. The system achieved 93% face-recognition accuracy with an average time of 2.7 seconds per person for identity verification when it was used for verifying 1,547 attendees of a concert of a popular music singer. The system made it possible to complete identity verification with higher accuracy with shorter average time than Ticket ID system.