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.
