Face Recognition and Emotion Detection

This Face recognition and facial emotion detection is new era of technology. It’s also indirectly defining the level of growth in intelligence, security and copying human emotional behaviour. It is mainly used in market research and testing. Many companies require a good and accurate testing method which contributes to their development by providing the necessary insights and drawing the accurate conclusions. Facial expression recognition technology can be developed through various methods. This technology can be developed by using the deep learning with the convolutional neural network or with inbuilt libraries like deepface. The main objective here is to classify each face based on the emotions shown into seven categories which include Anger, Disgust, Fear, Happiness, Sadness, Surprise and Neutrality. The main objective here in this project is, to read the facial expressions of the people and displaying them the product which helps in determining their interest in it. Facial expression recognition technology can also be used in video game testing. During the video game testing, certain users are asked to play the game for a specified period and their expressions, and their behavior are monitored and analyzed. The game developers usually use the facial expression recognition and get the required insights and draw the conclusions and provide their feedback in the making of the final product. In this project, deep learning with the convolutional neural networks (CNN) approach is used. Neural networks need to be trained with large amounts of data and have a higher computational power [8-11]. It takes more time to train the model.[1]