Context-Aware Emotion Recognition Networks (1908.05913v1)

Abstract: Traditional techniques for emotion recognition have focused on the facial expression analysis only, thus providing limited ability to encode context that comprehensively represents the emotional responses. We present deep networks for context-aware emotion recognition, called CAER-Net, that exploit not only human facial expression but also context information in a joint and boosting manner. The key idea is to hide human faces in a visual scene and seek other contexts based on an attention mechanism. Our networks consist of two sub-networks, including two-stream encoding networks to seperately extract the features of face and context regions, and adaptive fusion networks to fuse such features in an adaptive fashion. We also introduce a novel benchmark for context-aware emotion recognition, called CAER, that is more appropriate than existing benchmarks both qualitatively and quantitatively. On several benchmarks, CAER-Net proves the effect of context for emotion recognition. Our dataset is available at http://caer-dataset.github.io.

• The paper introduces CAER-Net, a novel two-stream framework that fuses facial and contextual cues to improve emotion recognition accuracy.
• It employs a 3D CNN for facial encoding and an attention-based module to extract significant contextual features.
• Experimental evaluations on the CAER and benchmark datasets show that integrating context markedly enhances recognition performance.

Context-Aware Emotion Recognition Networks

The paper "Context-Aware Emotion Recognition Networks" introduces a novel framework for emotion recognition that extends beyond traditional facial expression analysis by incorporating contextual information to enhance accuracy. This framework, called CAER-Net, leverages deep learning architectures to fuse both facial and contextual features, addressing the limitations of previous approaches that solely focused on facial expressions.

Methodology

CAER-Net is structured as a two-stream neural network model comprising a face encoding stream and a context encoding stream. Each stream is designed to independently extract relevant features from facial expressions and context respectively:

1. Face Encoding Stream: This component extracts facial expression features utilizing 3D convolutional neural networks (CNNs), which are adept at capturing spatiotemporal dynamics in video data.
2. Context Encoding Stream: Diverging from the facial stream, this stream leverages an attention mechanism to emphasize salient context information in the visual scene by intentionally disregarding facial regions during feature extraction. This encourages the network to focus on other contextual cues such as body language, interaction scenarios, and environmental elements.

Following feature extraction, an adaptive fusion network combines these feature sets to determine the overall emotional classification, employing an attention module to assign appropriate weights to the importance of face and context features during emotion inference.

Benchmark Dataset

The researchers developed a new dataset, the Context-Aware Emotion Recognition (CAER) dataset, to better capture the complexities of real-world emotion recognition. This dataset was compiled from TV show clips and is annotated with seven emotion categories. The dataset aims to provide richer context scenarios than existing datasets, which predominantly emphasize facial expressions alone, thereby offering a more nuanced benchmark for training and evaluating emotion recognition systems.

Experimental Evaluation

The performance of CAER-Net was evaluated on the CAER dataset and other benchmarks like AFEW. It demonstrated superior performance over both handcrafted feature-based methods and other deep learning approaches that exclusively use facial information. The inclusion of contextual cues was observed to significantly improve the recognition accuracy, highlighting the method's potential in real-world applications where accurate emotional understanding is critical.

Implications and Future Directions

The CAER-Net framework showcases the importance of incorporating broader context into emotion recognition systems, promoting a more comprehensive understanding of human emotions. The findings suggest pathways for future research to explore context integration, potentially incorporating multimodal signals such as audio, or extending the attention mechanism to dynamically prioritize different contextual elements based on scene variations.

This research paves the way for advanced human-computer interaction systems that require nuanced emotional assessment, such as AI-driven health monitoring and intelligent tutoring systems. Future developments could refine and expand upon these ideas, potentially facilitating even more robust emotion recognition frameworks capable of seamlessly understanding complex human emotions in open-world environments.