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Emotion is a scorching topic in the recent years due to the critical unseen stress incurred during the pandemic and post-pandemic. This is worsening with the recent economy’s inflation and increase of living cost, many employees are seriously affected and drawn forth many families saddened cases and tremendous drop of working performance. The increasing stress brings a lot of harm not only to the individual but to the company’s and country’s growth. To recognize emotion through a single model is less accurate, however, recruiting multiple-models may lead to latency in data processing and possibly misleading results if the input models data are not properly filtered and segmented. This paper will review, analyze and theoretically compare 15 facial expression methods and 17 voice methods of emotion recognition research works. It will outline the pros and cons of each method and discuss the accuracy of some of the standalone and hybrid emotion recognition methods. Some of the methods (such as CNN, KNN and SVM) can span over multiple-models, but reveal different level of strengths. This is very important to discover, so that one may replace or enhance the weaker level if applying the same method across the multiple-models. This paper will also illustrate different levels of popularity of the methods in each model for visual comparison in ease. Hopefully, it can cater the new researchers a quick identification on the most suitable method for recognizing the emotion through facial expression and/or voice.
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