Improving Face Recognition for Identity Verification by Managing Facial Directions and Eye Contact of Event Attendees

Abstract

This paper proposes an identity-verification system for attendees of large-scale events using continuous face recognition improved by managing facial directions and eye contact (eyes are open or closed) of the attendees. Identity-verification systems have been required to prevent illegal resale such as ticket scalping. The problem in verifying ticket holders is how to simultaneously verify identities efficiently and prevent individuals from impersonating others at a large-scale event at which tens of thousands of people participate. We previously developed two ticket ID systems for identifying the purchaser and holder of a ticket. These systems use two face-recognition systems, i.e., one-stop face-recognition system with a single camera and non-stop face-recognition system with two cameras. The average face-recognition accuracy was respectively 90 and 91%, and the average time for identity verification from check-in to entry admission was respectively 7 and 2.8 seconds per person. One-stop systems have lower equipment cost than non-stop systems because they require fewer cameras for face recognition. Since both systems were proven effective for preventing illegal resale by verifying attendees of large concerts, they have been used at more than 110 concerts. The problem with both systems 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 the single camera after an interval of about 0.5 seconds 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 they were used for verifying 8,461 attendees of a concert of a popular music singer. The system made it possible to complete identity verification with higher accuracy than previous systems and with shorter average time than the non-stop system using a single camera, i.e., with low equipment cost. Survey results obtained from the attendees showed that 96.4% felt it provided more equity in ticket purchasing than methods without face recognition, 87.1% felt it provided added convenience in verification, and 95.4% felt it would effectively prevent illegal resale.

Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inverse

“…This experiment trains a CNN model with seven convolution layers each with twenty [5,5] filters, seven batch normalization layers to normalize the data stream and speed up the training, seven rectified linear unit layers, and seven average pooling layers each with a pooling size of [2,2]. The model training uses a human facial image database, and the trained model can recognize gender for a given input image.…”

“…We live in a world of data and information, where image data plays an essential role in information sharing and transmitting. Learning from images empowers enormous AI tasks like objective detection, recognition, classification, and reconstruction [1][2][3][4], whose applications have already permeated the consumer market. However, there has been a growing privacy concern over the recent years as image databases widely accumulated, and machine/deep learning agents strengthen their capacity in extracting information from images, deteriorating the concern further [5].…”