Predicting Social Perception from Faces: A Deep Learning Approach (1907.00217v1)

Abstract: Warmth and competence represent the fundamental traits in social judgment that determine emotional reactions and behavioral intentions towards social targets. This research investigates whether an algorithm can learn visual representations of social categorization and accurately predict human perceivers' impressions of warmth and competence in face images. In addition, this research unravels which areas of a face are important for the classification of warmth and competence. We use Deep Convolutional Neural Networks to extract features from face images and the Gradient-weighted Class Activation Mapping (Grad CAM) method to understand the importance of face regions for the classification. Given a single face image the trained algorithm could correctly predict warmth impressions with an accuracy of about 90% and competence impressions with an accuracy of about 80%. The findings have implications for the automated processing of faces and the design of artificial characters.