Emotion Recognition Technology: Decoding Facial Expressions

Human emotions are pivotal in communication, and their recognition holds significance across various domains like human-computer interaction and mental health monitoring. This paper presents an image-based approach to emotion recognition, particularly focusing on discerning genuine from insincere emotions. The system uses a combination of machine learning (ML) and deep learning (DL) approaches to improve the accuracy and robustness of emotion recognition by utilizing data from several modalities. Utilizing algorithms such as CNN for facial expression recognition enables the system to extract quantifiable features, thereby improving its capability to distinguish between genuine and fake emotions. Evaluation involves testing with diverse datasets and performance metrics, particularly emphasizing the system's efficacy in detecting fake emotions. The proposed system is able to identify overall 80% of the accuracy over the Decision Tree, SVM and CNN models.