Towards Automated Classification of Emotional Facial Expressions

Emotional state influences nearly every aspect of human cognition. However, coding emotional state is a costly process that relies on proprietary software or the subjective judgments of trained raters, highlighting the need for a reliable, automatic method of recognizing and labeling emotional expression. We demonstrate that machine learning methods can approach near-human levels for categorization of facial expression in naturalistic experiments. Our results show relative success of models on highly controlled stimuli and relative failure on less controlled images, emphasizing the need for real-world data for application to real-world experiments. We then test the potential of combining multiple freely available datasets to broadly categorize faces that vary across age, race, gender and photographic quality.