Coupled source domain targetized with updating tag vectors for micro-expression recognition

Abstract

Micro-expression has raised increasing attention for analyzing human inner emotions. However, most micro-expression recognition methods are developed with specific feature representations and extraction methods, such as local binary pattern on three orthogonal planes (LBP-TOP) and optical flow. The performance in such micro-expression recognition models is not high due to the limited training samples and the unequal size of the sample category. To improve the performance, we present a novel algorithm, named coupled source domain targetized with updating tag vectors, and we apply it to the micro-expression recognition. This method leverages rich speech data to enhance micro-expression recognition by transferring learning from the speech to the micro-expression recognition. The method highlights are: it simultaneously projects micro-expression samples and speech samples into a common space, then minimizes the reconstruction error between the speech and micro-expression samples, with an updating tag vectors added in the reconstruction process. It performs recognition by using dictionary learning together with support vector machine (SVM). Experimental results on the CASIA Chinese emotional corpus and CASME II micro-expression database demonstrate the effectiveness of our method.