Exploring Mental Prototypes by an Efficient Interdisciplinary Approach: Interactive Microbial Genetic Algorithm

Abstract

Facial expression-based technologies have flooded our daily lives. However, most technologies are limited to Ekman's basic facial expressions and rarely deal with more than ten emotional states. This is not only due to the lack of prototypes for complex emotions but also the time-consuming and laborious task of building an extensive labeled database. To remove these obstacles, we were inspired by a psychophysical approach for affective computing, so-called the reverse correlation process (RevCor), to extract mental prototypes of what a given emotion should look like for an observer. We proposed a novel, efficient, and interdisciplinary approach called Interactive Microbial Genetic Algorithm (IMGA) by integrating the concepts of RevCor into an interactive genetic algorithm (IGA). Our approach achieves four challenges: online feedback loop, expertise-free, velocity, and diverse results. Experimental results show that for each observer, with limited trials, our approach can provide diverse mental prototypes for both basic emotions and emotions that are not available in existing deep-learning databases. Our work is available at https://yansen0508.github.io/Interactive-Microbial-Genetic-Algorithm/.