2014 Canadian Conference on Computer and Robot Vision (CRV) (2014)
Montreal, QC, Canada
May 6, 2014 to May 9, 2014
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CRV.2014.21
Recently, deep neural networks have been shown to perform competitively on the task of predicting facial expression from images. Trained by gradient-based methods, these networks are amenable to "multi-task" learning via a multiple term objective. In this paper we demonstrate that learning representations to predict the position and shape of facial landmarks can improve expression recognition from images. We show competitive results on two large-scale datasets, the ICML 2013 Facial Expression Recognition challenge, and the Toronto Face Database.
Face recognition, Face, Training, Neural networks, Feature extraction, Standards, Eyebrows
T. Devries, K. Biswaranjan and G. W. Taylor, "Multi-task Learning of Facial Landmarks and Expression," 2014 Canadian Conference on Computer and Robot Vision (CRV), Montreal, QC, Canada, 2014, pp. 98-103.