SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression from Long Videos

Abstract

Micro-expression is a subtle and involuntary facial expression that may reveal the hidden emotion of human beings. Spotting micro-expression means to locate the moment when the microexpression happens, which is a primary step for micro-expression recognition. Previous work in microexpression expression spotting focus on spotting micro-expression from short video, and with hand-crafted features. In this paper, we present a methodology for spotting micro-expression from long videos. Specifically, a new convolutional neural network named as SMEConvNet (Spotting Micro-Expression Convolutional Network) was designed for extracting features from video clips, which is the first time that deep learning is used in micro-expression spotting. Then a feature matrix processing method was proposed for spotting the apex frame from long video, which uses a sliding window and takes the characteristics of micro-expression into account to search the apex frame. Experimental results demonstrate that the proposed method can achieve better performance than existing state-of-art methods.