Predicting Social Perception from Faces: A Deep Learning Approach
This work addresses the automated processing of faces for social perception, with implications for designing artificial characters, but it is incremental as it applies existing methods to a specific domain.
This research tackled the problem of predicting human impressions of warmth and competence from face images using a deep learning approach, achieving accuracies of about 90% for warmth and 80% for competence.
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.