  Affective computing stands at the forefront of artificial intelligence (AI),
seeking to imbue machines with the ability to comprehend and respond to human
emotions. Central to this field is emotion recognition, which endeavors to
identify and interpret human emotional states from different modalities, such
as speech, facial images, text, and physiological signals. In recent years,
important progress has been made in generative models, including Autoencoder,
Generative Adversarial Network, Diffusion Model, and Large Language Model.
These models, with their powerful data generation capabilities, emerge as
pivotal tools in advancing emotion recognition. However, up to now, there
remains a paucity of systematic efforts that review generative technology for
emotion recognition. This survey aims to bridge the gaps in the existing
literature by conducting a comprehensive analysis of over 320 research papers
until June 2024. Specifically, this survey will firstly introduce the
mathematical principles of different generative models and the commonly used
datasets. Subsequently, through a taxonomy, it will provide an in-depth
analysis of how generative techniques address emotion recognition based on
different modalities in several aspects, including data augmentation, feature
extraction, semi-supervised learning, cross-domain, etc. Finally, the review
will outline future research directions, emphasizing the potential of
generative models to advance the field of emotion recognition and enhance the
emotional intelligence of AI systems.
