Humans tend to form quick subjective first impressions of non-physical attributes when seeing someone's face, such as perceived trustworthiness or attractiveness. To understand what variations in a face lead to different subjective impressions, this work uses generative models to find semantically meaningful edits to a face image that change perceived attributes. Unlike prior work that relied on statistical manipulation in feature space, our end-to-end framework considers trade-offs between preserving identity and changing perceptual attributes. It maps identity-preserving latent space directions to changes in attribute scores, enabling transformation of any input face along an attribute axis according to a target change. We train on real and synthetic faces, evaluate for in-domain and out-of-domain images using predictive models and human ratings, demonstrating the generalizability of our approach. Ultimately, such a framework can be used to understand and explain biases in subjective interpretation of faces that are not dependent on the identity.
View on arXiv@article{roygaga2025_2309.15381,
title={ Subjective Face Transform using Human First Impressions },
author={ Chaitanya Roygaga and Joshua Krinsky and Kai Zhang and Kenny Kwok and Aparna Bharati },
journal={arXiv preprint arXiv:2309.15381},
year={ 2025 }
}