A real time classifier for emotion and stress recognition in a vehicle driver

Recently there is a great interest in artificial systems able to understand and recognize human emotions. In this paper an Emotion Recognition System based on classical neural networks and neuro-fuzzy classifiers is proposed. Emotion recognition is performed in real time starting from a video stream acquired by a common webcam monitoring the user´s face. Neurofuzzy classifiers, in comparison with Multi Layer Perceptron trained by EBP algorithm, show very short training times, allowing applications with easy and automated set up procedures, to be used in a wide range of applications, from entertainment to safety. The algorithm yields very interesting performances and can be adopted to recognize emotions as well as possible pathological conditions of the individual to be monitored.