Facial Emotion Detection Using Haar Cascade and CNN Algorithm

Due to its wide applications in psychology, healthcare, and safety, facial emotion recognition is a crucial machine vision issue that has to be studied. Emotion detection from facial expressions is considered a challenging task because it involves numerous sophisticated processes, including the detection, analysis, and recognition of facial characteristics. In this research, the Haar Cascade algorithm and the Convolutional Neural Network (CNN) are combined to create an innovative approach for identifying facial expressions of emotion. The Haar Cascade algorithm is used to detect facial characteristics, like eyes, nose, and mouth, from an input image. These detected features are then fed into the CNN algorithm, which is responsible for recognising the person's state of mind. The suggested strategy is tested on a publicly available dataset, and the findings reveal that it achieves 98.42% accuracy. This indicates that the approach is highly effective in accurately detecting facial emotions.