AI based SVM: A novel method for emotion prediction and analysis

Human faces display a variety of emotions that highlight the person's mood and feelings. If work is to be provided in accordance with an individual's mood following emotion detection, facial expression recognition plays a crucial role in the productivity output. This paper examines the use of support vector machines, a type of artificial intelligence approach, to predict basic facial expressions like happiness, sadness, and rage. Because of its superior classification accuracy and ability to handle high-dimensional data, the SVM classifier is utilized. The suggested approach offers encouraging results in emotion classification, according to experimental study. The Yale Dataset, which includes a variety of facial image sets with different expressions, is utilized. The test data is fed into a support vector machine classifier using the Orange tools, which extracts features from photos that correspond to the fundamental emotions of human faces. Support Vector Machines perform better.

Human Emotion, Facial expression, Image processing, Facial Expression, Support Vector Machine.