Multi-Cue Adaptive Emotion Recognition Network

TL;DR

This paper introduces a deep learning model that combines facial expressions, body poses, and contextual cues for more accurate emotion recognition in unconstrained social interactions, outperforming existing methods.

Contribution

The paper presents a novel multi-cue adaptive emotion recognition network that integrates context and body pose information, improving accuracy over state-of-the-art approaches.

Findings

Achieved 89.30% accuracy on CAER-S dataset.

Demonstrated improved robustness in unconstrained scenarios.

Validated the effectiveness of multi-cue integration.

Abstract

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%