An Improved Non-Face-to-Face, Contactless Preference Survey System

Conventional election-related public opinion polls have utilized the automated response system (ARS) method. The ARS public opinion polls are predicated on the convenience of use and require random telephonic responses. However, the actual response rate is less than 5%. As a result, discrepancies between recent public opinion polls and the actual election results have become an issue. In this study, we propose a system that quantifies the preferences by region, age, and gender by quantifying emotions based on the behaviors and facial expressions of the citizens passing by at the campaign site and utilizes them as basic statistics. Furthermore, a previously published facial recognition artificial intelligence (AI) was used to obtain age, gender, and various facial recognition data, along with citizens’ emotions. The published facial recognition AI produced stability of over 99% recognition rate. The data structure followed a weighted reverse tree structure, and facial expressions, gender, and age were analyzed using the published facial recognition algorithm. Moreover, the expressions as well as the behaviors showing emotions were merged to gather and analyze data with weights