Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods

TL;DR

This paper investigates the use of various GAN architectures to generate emotionally evocative images for affective computing, aiming to create efficient and effective stimulus datasets.

Contribution

It introduces a comprehensive evaluation of multiple GAN models for producing emotionally charged images, advancing dataset creation methods in affective computing.

Findings

GANs can generate emotionally evocative images with promising quality.

Different GAN architectures vary in effectiveness for emotion induction.

Synthetic images have potential to supplement traditional affective datasets.

Abstract

Experiments in affective computing are based on stimulus datasets that, in the process of standardization, receive metadata describing which emotions each stimulus evokes. In this paper, we explore an approach to creating stimulus datasets for affective computing using generative adversarial networks (GANs). Traditional dataset preparation methods are costly and time consuming, prompting our investigation of alternatives. We conducted experiments with various GAN architectures, including Deep Convolutional GAN, Conditional GAN, Auxiliary Classifier GAN, Progressive Augmentation GAN, and Wasserstein GAN, alongside data augmentation and transfer learning techniques. Our findings highlight promising advances in the generation of emotionally evocative synthetic images, suggesting significant potential for future research and improvements in this domain.