Issue No. 11 - November (2009 vol. 31)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.42
Marian Bartlett , University of California, San Diego, La Jolla
Gwen Littlewort , University of California, San Diego, La Jolla
Ian Fasel , University of Arizona, Tucson
Javier Movellan , University of California, San Diego, La Jolla
Jacob Whitehill , University of California, San Diego, La Jolla
Machine learning approaches have produced some of the highest reported performances for facial expression recognition. However, to date, nearly all automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled lighting conditions on a relatively small number of subjects. This paper explores whether current machine learning methods can be used to develop an expression recognition system that operates reliably in more realistic conditions. We explore the necessary characteristics of the training data set, image registration, feature representation, and machine learning algorithms. A new database, GENKI, is presented which contains pictures, photographed by the subjects themselves, from thousands of different people in many different real-world imaging conditions. Results suggest that human-level expression recognition accuracy in real-life illumination conditions is achievable with machine learning technology. However, the data sets currently used in the automatic expression recognition literature to evaluate progress may be overly constrained and could potentially lead research into locally optimal algorithmic solutions.
Face and gesture recognition, machine learning, computer vision.
Marian Bartlett, Gwen Littlewort, Ian Fasel, Javier Movellan, Jacob Whitehill, "Toward Practical Smile Detection", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 31, no. , pp. 2106-2111, November 2009, doi:10.1109/TPAMI.2009.42