In this paper, we develop a new method that recognizes facial expressions, on the basis of an innovative local motion patterns feature, with three main contributions. The first one is the analysis of the face skin temporal elasticity and face deformations during expression. The second one is a unified approach for both macro and micro expression recognition. And, the third one is the step forward towards in-the-wild expression recognition, dealing with challenges such as various intensity and various expression activation patterns, illumination variation and small head pose variations. Our method outperforms state-of-the-art methods for micro expression recognition and positions itself among top-rank state-of-the-art methods for macro expression recognition.
As shown in Table , well-known static methods like LBP have limited potential for micro expression recognition. The difference would be attributable to the fact that it cannot discriminate very low intensity motions @cite_0 . LBP-TOP has shown promising performance for facial expression recognition. Therefore, many researchers have actively focused on the potential ability of LBP-TOP for micro expression recognition.
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Micro-expressions (MEs) are rapid, involuntary facial expressions which reveal emotions that people do not intend to show. Studying MEs is valuable as recognizing them has many important applications, particularly in forensic science and psychotherapy. However, analyzing spontaneous MEs is very challenging due to their short duration and low intensity. Automatic ME analysis includes two tasks: ME spotting and ME recognition.For ME spotting, previous studies have focused on posed rather than spontaneous videos. For ME recognition, the performance of previous studies is low. To address these challenges, we make the following contributions: (i) We propose the first method for spotting spontaneous MEs in long videos (by exploiting feature difference contrast). This method is training free and works on arbitrary unseen videos. (ii) We present an advanced ME recognition framework, which outperforms previous work by a large margin on two challenging spontaneous ME databases (SMIC and CASMEII). (iii) We propose the first automatic ME analysis system (MESR), which can spot and recognize MEs from spontaneous video data. Finally, we show that our method achieves comparable performance to humans at this very challenging task, and outperforms humans in the ME recognition task by a large margin.