Predicting First Impressions with Deep Learning
This work addresses the challenge of modeling subjective attributes like trustworthiness or intelligence from faces, which is important for applications in psychology and affective computing, representing a novel extension beyond objective facial analysis.
The paper tackles the problem of predicting subjective first impressions from facial images, where no ground truth exists, by introducing a convolutional neural network-based regression framework that models crowd behavior for social attribute assignment, achieving strong correlations with human ratings on the AFLW face database.
Describable visual facial attributes are now commonplace in human biometrics and affective computing, with existing algorithms even reaching a sufficient point of maturity for placement into commercial products. These algorithms model objective facets of facial appearance, such as hair and eye color, expression, and aspects of the geometry of the face. A natural extension, which has not been studied to any great extent thus far, is the ability to model subjective attributes that are assigned to a face based purely on visual judgements. For instance, with just a glance, our first impression of a face may lead us to believe that a person is smart, worthy of our trust, and perhaps even our admiration - regardless of the underlying truth behind such attributes. Psychologists believe that these judgements are based on a variety of factors such as emotional states, personality traits, and other physiognomic cues. But work in this direction leads to an interesting question: how do we create models for problems where there is no ground truth, only measurable behavior? In this paper, we introduce a new convolutional neural network-based regression framework that allows us to train predictive models of crowd behavior for social attribute assignment. Over images from the AFLW face database, these models demonstrate strong correlations with human crowd ratings.