  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.
