Automatic emotion analysis and understanding has received much attention over the years in affective computing. One of the key modalities is facial expressions. CMVS has done extensive research on facial expression recognition using dynamic information analysis. Our experimental results also demonstrate promising performance for group happiness intensity analysis.
A goal of our current research is in developing methodology for recognizing spontaneous expressions from continuous video data in unconstrained conditions (“in the wild”).
Zhao G & Pietikäinen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6):915-928.
Huang X, Dhali A, Zhao G, Goecke R & Pietikäinen M (2015) Riesz-based volume local binary pattern and a novel group expression model for group happiness intensity analysis. Proc. the British Machine Vision Conference, Swansea, UK, 13 p.
Guo Y, Zhao G & Pietikäinen M (2016) Dynamic facial expression recognition with atlas construction and sparse representation. IEEE Transactions on Image Processing 25(5):1977-1992.
Last updated: 22.8.2016