Comparative Analysis of Micro Expression Recognition using Deep Learning and Transfer Learning

Micro Expressions are those involuntary muscular movements of the facial muscles produced in response to a stimulus. They are short-lived expressions that last for anywhere between 0.04 to 0.2 seconds and are extremely subtle in their amplitude. Given their fleeting and elusive nature, it becomes almost impossible to detect these expressions through the naked eye. Recent developments in Deep Learning models have shown great success in efficiently identifying and analyzing Micro Expressions. In this paper, various models have been implemented on the SAMM dataset. The models studied are namely– VGG16, ResNet50, MobileNet, InceptionV3, and Xception. The experimental results have helped us carefully analyze the various metrics related to the models and compare them with each other to ascertain which one outperformed the others and is best suited for real-world applications. The MobileNet model has surpassed all other models in terms of its efficiency with respect to the domain of this paper. It has been able to describe and understand all the information that can be found in the various Micro Expressions.