Deep Learning for Emotion Recognition: From Theory to Practice

This book investigates developments in computer vision and artificial intelligence automated emotional perception. Specifically, we use deep learning, DCNN, and VGG19 algorithms to combine body language and contextual information, including environmental, social, and cultural factors. We optimize deep neural networks by aggregating many picture datasets, including EMOTIC (ADE20K, MSCOCO), EMODB_SMALL, and FRAMESDB, to evaluate continuous emotional dimensions and discrete emotions properly. Our results show notable progress over current methods, improving contextual emotional awareness. This work opens the path for significant applications in social robotics, affective computing, and human-machine interaction, enabling complex emotional sensing in many different real-world contexts.

Autorentext

Dr. Fatiha Limami, a Ph.D. candidate at ENSIAS, Rabat, Morocco, specializing in data science, big data, and artificial intelligence. Her research interests focus on deep learning for emotion recognition, aiming to develop context-aware systems beneficial in social robotics, affective computing, and HCI applications.