Subjective Face Transform using Human First Impressions

• URL: http://arxiv.org/abs/2309.15381v1
• Date: Wed, 27 Sep 2023 03:21:07 GMT
• Title: Subjective Face Transform using Human First Impressions
• Authors: Chaitanya Roygaga, Joshua Krinsky, Kai Zhang, Kenny Kwok, Aparna Bharati
• Abstract summary: This work uses generative models to find semantically meaningful edits to a face image that change perceived attributes. We train on real and synthetic faces, evaluate for in-domain and out-of-domain images using predictive models and human ratings.
• Score: 5.026535087391025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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