Controlling Memorability of Face Images

TL;DR

This paper introduces a fast method to modify and control the memorability of face images by manipulating their position in the StyleGAN latent space, enabling applications in social media, education, and advertising.

Contribution

It proposes a novel approach to adjust face image memorability by finding a hyperplane in StyleGAN's latent space and moving images along this hyperplane's normal vector.

Findings

The method effectively controls memorability of real and synthetic faces.

Different StyleGAN layers contribute variably to face memorability.

The approach maintains identity and facial attributes while modifying memorability.

Abstract

Everyday, we are bombarded with many photographs of faces, whether on social media, television, or smartphones. From an evolutionary perspective, faces are intended to be remembered, mainly due to survival and personal relevance. However, all these faces do not have the equal opportunity to stick in our minds. It has been shown that memorability is an intrinsic feature of an image but yet, it is largely unknown what attributes make an image more memorable. In this work, we aimed to address this question by proposing a fast approach to modify and control the memorability of face images. In our proposed method, we first found a hyperplane in the latent space of StyleGAN to separate high and low memorable images. We then modified the image memorability (while maintaining the identity and other facial features such as age, emotion, etc.) by moving in the positive or negative direction of this hyperplane normal vector. We further analyzed how different layers of the StyleGAN augmented latent space contribute to face memorability. These analyses showed how each individual face attribute makes an image more or less memorable. Most importantly, we evaluated our proposed method for both real and synthesized face images. The proposed method successfully modifies and controls the memorability of real human faces as well as unreal synthesized faces. Our proposed method can be employed in photograph editing applications for social media, learning aids, or advertisement purposes.