Weakly-supervised Learning for Fine-grained Emotion Recognition using Physiological Signals

Instead of predicting just one emotion for one activity (e.g., video watching), fine-grained emotion recognition enables more temporally precise recognition. Previous works on fine-grained emotion recognition require segment-by-segment, fine-grained emotion labels to train the recognition algorithm. However, experiments to collect these...

Towards Artificial Empathic Memory: Accounting for the Influence of Personal Memories in Automatic Predictions of Affect

ET-CycleGAN: Generating thermal images from images in the visible spectrum for facial emotion recognition

Facial thermal imaging has in recent years shown to be an efficient modality for facial emotion recognition. However, the use of deep learning in this field is still not fully exploited given the small number and size of the current datasets. The goal of this work is to improve the performance of the existing deep networks in thermal facial...

Corrnet: Fine-grained emotion recognition for video watching using wearable physiological sensors

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion...

Extracting emotions from face-to-face communication

In our life we get more and more dependent on our computer and we have less time for face-to-face social activities with friends and families. In face?to?face communication our faces convey lots of emotions using facial expressions and lots of information is transmitted faster non-verbally than verbally, through the facial expressions. Using...