@inproceedings{b23e5c41e6404b78ae28e1172ca7444a,
title = "Facial action unit recognition using recurrent neural networks",
abstract = "Facial expression recognition has been a major area of research in the field of computer vision and human computer interaction for more than ten years. Recently more emphasis has been laid on the individual muscle changes and their effect on the face, than the expressions themselves. The facial action coding system describes the appearance changes in the face at the muscular level with a set of action units. In this paper, we attempt to recognize these action units from image sequences. Feature extraction of the images is performed using Gabor filters, while classification is done using recurrent neural networks which have the ability to handle time-variant data. A set of 6 upper face action units and 5 lower face action units are recognized. An average recognition rate of 83.51% was achieved for the 6 upper face action units and 81.98% for the 5 lower face action units. The overall recognition rate of 82.75% was achieved for all the 11 action units.",
keywords = "Facial action unit recognition, Feature extraction, Gabor filters, Recurrent neural networks",
author = "Vadapalli, {H. B.} and H. Nyongesa and Omlin, {C. W.P.}",
year = "2009",
language = "English",
isbn = "9781601321190",
series = "Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009",
pages = "357--361",
booktitle = "Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009",
note = "2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009 ; Conference date: 13-07-2009 Through 16-07-2009",
}