Description of Invention
Micro-expression (ME) is a form of reflexive behaviour and indirect communication in which an individual expresses their emotions via facial muscle movements. Due to the nature of ME that only appears for a fraction of a second, and even a trained individual will have a hard time detecting and recognizing it. In this work, a transfer learning approach is utilized to recognize the static facial micro-expression images. Six pre-trained convolutional neural network (CNN) models, including the AlexNet, SqueezeNet, GoogleNet, EfficientNet-b0, ResNet 50, and MobileNet-v2 are employed and evaluated on the improved version of the Chinese Academy of Sciences Micro-expression (CASME II) database.