Face Recognition and Smart People-Counting System: Cases of Asian Trade Shows

Abstract

Exhibitors are highly interested in visitors’ visitation, ordering, and buying behaviors in business-to-business (B2B) and business-to-consumer (B2C) trade shows. Previous studies on business trade shows have focused on various aspects, such as trade show selection, motivation, and performance and benefits. However, only a few empirical studies have examined the experiences of exhibitors in their participation in face recognition systems in trade shows. In the current study, face recognition software combined with a server linking the Internet of Things concepts was adopted. People flow tally and data collection were carried out in six in empirical cases of Asian trade shows. These cases include three B2B cases and three B2C cases in three Asian countries, namely Taiwan, China, and Japan, through experimentation and observation, coupled with video and scanning system set up at site exits and entrances. Results show that the face recognition system can precisely and timely provide distribution data on the number of people at an exhibition site, as well as their age, gender, and time of stay. The exhibitors can use data tally and information on the peak and off-peak hours of people flow or the cold and hot areas of the exhibition site to timely and dynamically adjust marketing activities.