In a number of applications, such as affective computing, healthcare, and human-computer interaction, facial expression recognition is essential. Researchers have created a number of algorithms and approaches to precisely identify and categorise facial expressions in response to the growing need for real-time emotion analysis. This research study includes a comparative examination of the effectiveness and performance of various methods for real-time facial emotion identification. Deep learning-based methods, feature-based methods, and hybrid models are some of the investigated methodologies. On accuracy, real-time processing capacity, computational complexity, and resistance to changes in illumination, face occlusions, and position changes, the comparison is made. The results of this study can help researchers and professionals choose the best algorithm for their particular application needs.
Key words: facial emotion recognition, real-time algorithms, deep learning, feature-based methods, hybrid models, comparative analysis.
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