Facial Micro-Expression Recognition Using Quaternion-Based Sparse Representation

Abstract

Facial micro-expressions are characterized by their extremely short duration and low intensity, can provide an important basis for judging people’s emotions, and therefore have promising potential applications in numerous fields. This paper puts forward a novel method for recognizing micro-expressions by using a quaternion-based sparse representation (QSR) model combined with the integral projection of difference energy image (IP-DEI) to extract features from color images of human faces . Using the quaternion model to jointly process color images can obtain greater feature information than gray or RGB images, and the QSR model helps reduce feature dimensions and enables greater discriminative representation. First, each micro-expression sample undergoes IP-DEI to allow the features of all samples to be displayed in the form of a quaternion matrix ${\mathbf{\dot Y}}$. Next we find overcomplete dictionary matrix ${\mathbf{\dot D}}$ and sparse coefficient matrix ${\mathbf{\dot X}}$ such that ${\mathbf{\dot Y}} = {\mathbf{\dot D\dot X}}$ in ideal scenarios, and consider ${\mathbf{\dot X}}$ to be the features contained within the micro-expression samples. Finally, we apply our method to the SMIC, CAMSE I and CAMSE II micro-expression databases while using SVM as classifier. The results of the experiment demonstrate that our method outperformed the currently most advanced methods in terms of micro-expression recognition accuracy.