Emotion Detection in Social Robotics: Empath-Obscura - An Ensemble Approach with Novel Face Augmentation Using SPIGA

Emotion recognition is a key component of human-computer interaction in social robotics. In this paper, we present Empath-Obscura, an innovative ensemble model designed to detect emotions in obfuscated faces. The model combines the cutting-edge object detection models YOLO V5 and V8 with the well-established Poster++ facial emotion recognition model. A significant contribution of this work is the development of a novel data augmentation technique that utilizes SPIGA, a shape-preserving facial landmark detection model, to selectively obscure facial features. This approach enhances the model's robustness against partially hidden facial expressions, improving the performance of the overall model by 13.18%. Empath-Obscura is rigorously validated on the FER-2013 dataset, which is well-suited for this study due to its representation of low-resolution and poor-quality facial images. A manually obfuscated and annotated test set further ensures accurate evaluation. The ensemble model achieved a remarkable accuracy of 69.3%, outperforming the individual models. The results presented in this paper, along with the innovation in our ensemble and data augmentation techniques, offer a significant contribution to the fields of social robotics and emotion recognition. This work provides researchers and practitioners with a robust and reliable tool for emotion detection from obfuscated faces, contributing to advancements in human-computer interaction for social robotics.