HUMAN EMOTION RECOGNITION USING DEEO LEARNING TECHNIQUES

Abstract

Human emotion recognition plays an important role in the interpersonal relationship. The automatic recognition of emotions has been an active research topic from early eras. Therefore, there are several advances made in this field. Emotions are reflected from speech, hand and gestures of the body and through facial expressions. Hence extracting and understanding of emotion has a high importance of the interaction between human and machine communication. The clinical, emotionless computer or robot is a staple of science fiction, but science fact is starting to change: computers are getting much better at understanding emotions. Automated customer service “bots” will be better able to know if a customer is getting the help they need. Robot caregivers involved with telemedicine may be able to detect pain or depression even if the patient doesn’t explicitly talk about it. Insurance companies are even experimenting with call voice analytics that can detect that someone is telling lies to their claims handers. This project will use deep learning techniques to detect human emotions from faces, since face is the prime source for recognizing human emotions. In particular, we used convolutional neural network(CNN) as the deep learning technique. Network was designed in Python language with the help of deep learning library by Google called TensorFlow without the CUDA framework.