Micro-expression is very rapid involuntary facial expressions which reveal suppressed affect. Facial micro-expressions reveal contradictions between facial expressions and the emotional state, enabling recognition of suppressed emotions. The method (and corpus) developed in CMVS is the first to recognize spontaneous facial micro-expressions and achieves very promising results.
We have also investigated methods for spotting micro-expressions from video data to be used, e.g. as a preprocessing step prior to recognition. An aim l of our current research is to make automated micro-expression analysis a practical tool for real-world applications.
Pfister T, Li X, Zhao G & Pietikäinen M (2011) Recognising spontaneous facial micro-expressions. Proc. International Conference on Computer Vision (ICCV 2011), Barcelona, Spain, 1449-1456.
Li X, Pfister T, Huang X, Zhao G & Pietikäinen M (2013) A spontaneous micro-expression database: Inducement, collection and baseline. Proc. IEEE International Conference on Face and Gesture Recognition (FG 2013), 1-6.
Moilanen A, Zhao G & Pietikäinen M (2014) Spotting rapid facial movements from videos using appearance-based feature difference analysis. Proc. 22nd International Conference on Pattern Recognition (ICPR), Stockholm, Sweden, 1722-1727.
Patel D, Zhao G & Pietikäinen M (2015) Spatiotemporal integration of optical flow vectors for micro-expression detection. In: Advanced Concepts for Intelligent Vision Systems, ACIVS 2015 Proceedings, Lecture Notes in Computer Science, 9386:369-380.
Huang X, Zhao G, Hong X, Zheng W & Pietikäinen M (2016) Spontaneous facial micro-expression analysis using spatiotemporal completed local quantized patterns. Neurocomputing, 175:564-578.
Last updated: 23.8.2016