Detection of Micro-expression Recognition Based on Spatio-Temporal Modelling and Spatial Attention

Abstract

My PhD project aims to make contributions in the affective computing application to assist in the depression diagnosis by micro-expression recognition. My motivation is the similarities of the low-intensity facial expressions in micro-expressions and the low-intensity facial expressions (`frozen face?) in people with psycho-motor retardation caused by depression. It will focus on, firstly, investigating spatio-temporal modelling and attention systems for micro-expression recognition (MER) and, secondly, exploring the role of micro-expressions in automated depression analysis by improving deep learning architectures to detect low-intensity facial expressions. This work will investigate different deep learning architectures (e.g. Temporal Convolutional Networks (TCNN) or Gate Recurrent Unit (GRU)) and validate the results on publicly available micro-expression benchmark datasets to quantitatively analyse the robustness and accuracy of MER's contribution to improving automatic depression analysis. Moreover, video magnification as a way to enhance small movements will be combined with the deep learning methods to address the low-intensity issues in MER.