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Contextual emotion detection in images using deep learning.

Fatiha Limami¹, Boutaina Hdioud¹, Rachid Oulad Haj Thami¹

¹Advanced Digital Enterprise Modeling and Information Retrieval (ADMIR) Research Laboratory, Information Retrieval and Data Analytics Team (IRDA), ENSIAS, Mohammed V University, Rabat, Morocco.

Frontiers in Artificial Intelligence

|July 2, 2024

Summary

This study enhances artificial intelligence (AI) for sentiment detection by integrating context and body language analysis. The developed deep learning models significantly improve the accuracy of recognizing emotions in images.

Keywords:

EMOTIC body language computer vision contextual recognition emotion recognition human–robot communication

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Area of Science:

Computer Science
Artificial Intelligence
Affective Computing

Background:

Computerized sentiment detection is crucial for understanding human emotions.
Advancements in deep neural networks enable AI to consider environmental, social, and cultural factors, alongside facial expressions.

Purpose of the Study:

To develop more empathetic AI systems for applications in medicine and social media analysis.
To improve the accuracy of emotion recognition in images by incorporating contextual and non-verbal cues.

Main Methods:

Utilized authentic image datasets (EMOTIC, EMODB_SMALL, FRAMESDB) for model training.
Developed deep learning algorithms, including DCNN and VGG19, optimizing hyperparameters for contextual understanding.
Merged discrete emotional categories with continuous emotional dimensions for comprehensive emotion identification.

Main Results:

Achieved significant performance improvements in sentiment recognition.
The Sentiment_recognition_model and VGG19_contexte models increased mean Average Precision (mAP) by 42.81% and 44.12%, respectively.
Outperformed previous methods in capturing diverse emotions across various contexts.

Conclusions:

The research offers a significant advancement in contextual emotion recognition within images.
Potential applications include social robotics, human-machine interaction, and human-robot communication.