Identifying Employee Emotions Based on Facial Expression Analysis in IT Sector using Ensemble Learning Techniques: A Systematic Literature Review

Facial expression analysis is an important tool for determining employee emotions, especially in the IT sector, where knowing emotional states can have significant effects on productivity and well-being. Traditional techniques for emotion detection are frequently limited by their dependence on simple recognition models that may not fully represent the complexities of human emotions. However, recent research has investigated the possibilities of ensemble learning techniques to improve the accuracy and dependability of these systems. This systematic literature review examines the various approaches used in emotion recognition from 2011 to 2023, with a focus on how ensemble learning might increase the efficiency of facial expression analysis. An initial selection of 33 studies was taken from six internet databases, with 7 eventually being included in the final review. The findings show that integrating several analytical methodologies into models produces promising results, leading the path for more robust emotion identification systems in the workplace.