Emotion Recognition by Using Machine Learning for Enhancing Human-Computer Interaction

Emotion recognition as the aspect of artificial intelligence is the ability of a system to understand people's emotions from information that includes face, voice, and text. More complex algorithms such as CNNs and RNNs are used to train the algorithms on considerable amounts of data to extract relevant features for high reliability and efficiency. Measures such as accuracy, and preciseness of the model often determine the usability of the models in practical applications. The proper implementation of these models has major repercussions in various domains, including human computer interaction, healthcare and sentiment analysis to name a few, changing the idea and approach towards emotions in the modern world. These approaches offer improved accuracy and robustness, setting the stage for more nuanced and reliable emotion recognition systems. We have gathered dataset for emotion recognition from Kaggle. We used a dataset that contains various features extracted from various emotions images, and our model resulted in a test accuracy of 95 %. This high level of accuracy demonstrates the capability of our model to effectively classify emotional states.