Face and Emotion Recognition in Real Time using Machine Learning

In present era there are various methods and different features which can be used to perform face recognition and emotion recognition like face, text, speech, and so on. The facial feature among these is the best. Emotion detection is also widely used in many fields like understanding human behaviour, detecting mental disorders, finding the emotions of people in a crowd, etc. In the proposed framework, face-emotion detection can be defined in three stages. The first stage is detecting the human face from the camera feed then in the second stage, the input is analysed based on the features with help of the convolutional neural networks (CNN) model. The last stage is to classify human emotion into 7 basic categories: fear, anger, disgust, sad, surprise, neutral and happy. Thus, proposed framework has mainly three objectives like face detection, face recognition and emotion classification. Face recognition has many significant applications in various fields like identity verification, security, biometrics, smart card, surveillance system, etc. Facial expression identification eminently comes from a few parts of the face, like eyes and mouth. The framework proposes to capture the facial features in real-time and process them to identify if there are any similarities between the real-time input and any of the trained faces. The outcome of the proposed framework is to display the name of the recognized individual and classify the face emotion with probabilities of the emotion. It also displays the emotion of an unknown person with the name given as ‘unidentified’, along with the probabilities of the emotions.