World Journal of Engineering Research and Technology

Sonika Katta* and Anil Pal

ABSTRACT

Emotion recognition plays an important role in increasing human-computer interactions, enables the system to explain and react to the user's feelings in a more human and sympathetic manner. This paper presents a comprehensive review of recent progress in easement by generative artificial intelligence (AI), which includes models such as Generative Adversarial networks (GANS), Variational Autoencoders (VAES), and large-scale transformer-based architecture. We check how generative AI increases data growth, multimodal synthesis and domain adaptation in various methods, such as facial expressions, speeches, lessons and physical signs. Additionally, we detect the challenges and boundaries of the current approaches, including issues related to dataset bias, generality, real -time processing and moral implications. By synthesizing insights from recent literature and technological progress, these reviews highlight the transformative ability of generic AI in emotion-comprehensive systems and promises future instructions for affectionate computing, mental health diagnosis and research and application in adaptive user interfaces.

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