EMOTION RECOGNITION BASED ON FACIAL EXPRESSIONS USING CONVOLUTIONAL NEURAL NETWORK

Abstract

Over the last years, there is a large number of studies focused in automatic facial expression analysis because of its practical importance in many human-computer interaction Systems. With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. In this thesis, we study the challenges of Emotion Recognition Datasets and try different parameters and architectures of the Conventional Neural Networks. The dataset we have used is iCV MEFED, a relatively new, interesting and very challenging.