Identification Emoticon Meaning using Facial Expression via Inception V3 as Well as KNN Methods

Recognising emotions in people is essential to understanding human behaviour since it allows for correct perception and suitable reactions to people’s emotional states. In order to get a deeper understanding of complicated human behaviour, researchers meticulously examine a variety of physical indications across modalities, such as facial expressions, verbal signals, and body language. The ability of spontaneous multi-modal emotion identification to collect and understand emotions in real time is shown by the current spike in interest in this field, highlighting the importance of combining audio input with facial expressions. In order to tackle the difficulties associated with speech emotion identification, this research makes use of the RAVDESS dataset and applies deep learning techniques, such as inception V3 and KNN models. The librosa package is used to extract spectrograms, and KNN models are employed to recognise speech emotions. The study also combines methods from video-based emotion recognition with point-wise convolution for frame analysis. The main goal of this project is to improve real-time emotion identification, which has potential uses in the fields of software engineering, gaming, education, and personalised online experiences. Metrics like accuracy, precision, and confusion matrix may be used to do a thorough assessment of the suggested multimodal emotion detection system.