Facial Expression Classification Based on Convolutional Neural Networks

Research trends in Convolutional Neural Networks and facial expression analysis are introduced at first. A training algorithm called stochastic gradient descent with l2 regularization is employed for the facial expression classification problem, in which facial expression images are classified into six basic emotional categories of anger, disgust, fear, happiness, sadness and surprise without any complex pre-processes involved. Moreover, three types of feature generalization for solving problems with different classifiers, different datasets and different categories are discussed. By these techniques, pre-trained Convolutional Neural Networks are used as feature extractors which work quite well with Support Vector Machine classifiers. The results of experiments show that Convolutional Neural Networks not only have capability of classifying facial expression images with translational distortions, but also have capability to fulfill some feature generalization tasks.

Keywords: Alex-Net architecture, Backpropagation algorithm, CK-Regianini dataset, CK-Zheng dataset, Classification accuracy, CMU-Pittsburgh dataset, Combined features, Convolutional Neural Networks, Deep learning, Facial expression classification, Feature extraction, Feature generalization, Feature representation, Hidden layers, Pre-trained networks, Stochastic Gradient Descent, Supervised feature learning, Support Vector Machine, Trainable parameters, Translational invariance property.