Deep Learning for Emotion Recognition

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.