Subtle expression recognition using optical strain weighted features

Abstract. Optical strain characterizes the relative amount of displace-ment by a moving object within a time interval. Its ability to compute any small muscular movements on faces can be advantageous to subtle expression research. This paper proposes a novel optical strain weighted feature extraction scheme for subtle facial micro-expression recognition. Motion information is derived from optical strain magnitudes, which is then pooled spatio-temporally to obtain block-wise weights for the spa-tial image plane. By simple product with the weights, the resulting fea-ture histograms are intuitively scaled to accommodate the importance of block regions. Experiments conducted on two recent spontaneous micro-expression databases – CASMEII and SMIC, demonstrated promising im-provement over the baseline results.