Multi-Cue Adaptive Emotion Recognition Network
This addresses the problem of limited emotion recognition in uncontrolled scenarios for applications in human-machine interaction.
The paper tackled emotion recognition in unconstrained daily interactions by proposing a deep learning approach that uses adaptive multi-cues from context and body poses, achieving an accuracy of 89.30% on the CAER-S dataset.
Expressing and identifying emotions through facial and physical expressions is a significant part of social interaction. Emotion recognition is an essential task in computer vision due to its various applications and mainly for allowing a more natural interaction between humans and machines. The common approaches for emotion recognition focus on analyzing facial expressions and requires the automatic localization of the face in the image. Although these methods can correctly classify emotion in controlled scenarios, such techniques are limited when dealing with unconstrained daily interactions. We propose a new deep learning approach for emotion recognition based on adaptive multi-cues that extract information from context and body poses, which humans commonly use in social interaction and communication. We compare the proposed approach with the state-of-art approaches in the CAER-S dataset, evaluating different components in a pipeline that reached an accuracy of 89.30%