Generation and Editing of Mandrill Faces: Application to Sex Editing and Assessment

TL;DR

This paper introduces a novel GAN-based method for generating and editing mandrill faces, specifically to modify sex, and provides a quantitative assessment of the editing accuracy, advancing animal face synthesis and analysis.

Contribution

It presents the first application of GAN-based face editing to a non-human primate, enabling sex modification and quantitative assessment of results.

Findings

Realistic mandrill face generation achieved

Accurate sex editing demonstrated

Quantitative assessment method developed

Abstract

Generative AI has seen major developments in recent years, enhancing the realism of synthetic images, also known as computer-generated images. In addition, generative AI has also made it possible to modify specific image characteristics through image editing. Previous work has developed methods based on generative adversarial networks (GAN) for generating realistic images, in particular faces, but also to modify specific features. However, this work has never been applied to specific animal species. Moreover, the assessment of the results has been generally done subjectively, rather than quantitatively. In this paper, we propose an approach based on methods for generating images of faces of male or female mandrills, a non-human primate. The main novelty of proposed method is the ability to edit their sex by identifying a sex axis in the latent space of a specific GAN. In addition, we have developed an assessment of the sex levels based on statistical features extracted from real image distributions. The experimental results we obtained from a specific database are not only realistic, but also accurate, meeting a need for future work in behavioral experiments with wild mandrills.