EMOTION DETECTION USING MACHINE LEARNING: ASSESSMENT OF EFFECTIVENESS AND APPLICABILITY IN DIFFERENT CONTEXTS

This research conducts a comprehensive analysis of emotion detection using facial expressions, employing various experiments and methodologies. The study initiates with a robust dataset comprising 35,685 grayscale facial images categorized by distinct emotions. Preliminary tests reveal a significant correlation between language learning settings and data patterns, demon-strating the model’s adaptability to diverse contexts. Nevertheless, the re-search transcends mere emotion detection, striving to fathom the intricacies of emotions across diverse scenarios. This investigation entails complex con-siderations, such as the role of neutral expressions as a positive emotion in-dicator and the substitution of emotion capture time with emotion propor-tion in long-term analyses. Various experiments are conducted, altering participant numbers and environmental conditions. The outcomes indicate that, within controlled environments, the system yields favorable results, but real-world simulations, including obstacles and inadequate lighting, present challenges. Depth analysis underscores complexities in representing three-dimensional objects in a two-dimensional space. This study underscores the promise of emotion detection via facial expressions while emphasizing the complexities and challenges across different contexts. The findings offer valuable insights for enhancing model accuracy and applicability in real-world scenarios, pinpointing avenues for future enhancements and invest-ments