Micro-Facial Movements: An Investigation on Spatio-Temporal Descriptors

This paper aims to investigate whether micro-facial movement sequences can be distinguished from neutral face sequences. As a micro-facial movement tends to be very quick and subtle, classifying when a movement occurs compared to the face without movement can be a challenging computer vision problem. Using local binary patterns on three orthogonal planes and Gaussian derivatives, local features, when interpreted by machine learning algorithms, can accurately describe when a movement and non-movement occurs. This method can then be applied to help aid humans in detecting when the small movements occur. This also differs from current literature as most only concentrate in emotional expression recognition. Using the CASME II dataset, the results from the investigation of different descriptors have shown a higher accuracy compared to state-of-the-art methods.