I am Only Happy When There is Light: The Impact of Environmental Changes on Affective Facial Expressions Recognition
This addresses robustness issues in human-robot interaction for deploying social robots in real-world environments, but it is incremental as it builds on existing methods.
The study investigated how environmental changes, such as lighting conditions, affect the recognition of arousal and valence from human facial expressions using the FaceChannel framework, finding that even slight image property alterations can significantly alter interpretation of affective states in either positive or negative directions.
Human-robot interaction (HRI) benefits greatly from advances in the machine learning field as it allows researchers to employ high-performance models for perceptual tasks like detection and recognition. Especially deep learning models, either pre-trained for feature extraction or used for classification, are now established methods to characterize human behaviors in HRI scenarios and to have social robots that understand better those behaviors. As HRI experiments are usually small-scale and constrained to particular lab environments, the questions are how well can deep learning models generalize to specific interaction scenarios, and further, how good is their robustness towards environmental changes? These questions are important to address if the HRI field wishes to put social robotic companions into real environments acting consistently, i.e. changing lighting conditions or moving people should still produce the same recognition results. In this paper, we study the impact of different image conditions on the recognition of arousal and valence from human facial expressions using the FaceChannel framework \cite{Barro20}. Our results show how the interpretation of human affective states can differ greatly in either the positive or negative direction even when changing only slightly the image properties. We conclude the paper with important points to consider when employing deep learning models to ensure sound interpretation of HRI experiments.