Facial Expressions Recognition through Convolutional Neural Network and Extreme Learning Machine

This paper investigates the facial expressions recognition approach. The image classification process is based on deep learning techniques for detecting emotional expressions. This study involves two main parts. Firstly, it highlights the technique of extracting significant features resulting on the use of a pre-trained convolutional neural network (Alexnet model). Afterward, The extracted learned features are trained through a back propagation (BP) and an extreme learning machine (ELM) algorithms. All experiments are implemented on the two available JAFFE and KDEF datasets. Simulation results show that Alexnet is an effective model for features extraction and the ELM approach produces a better training and generalization performance with a faster learning speed to well detecting facial expressions.