title: Toward practical smile detection.
abstract: 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.
authors.affiliation: Machine Perception Laboratory, University of California, San Diego, La Jolla, CA 92093-0440, USA. jake@mplab.ucsd.edu
authors.forename: Jacob
authors.initials: J
authors.lastname: Whitehill
authors.forename: Gwen
authors.initials: G
authors.lastname: Littlewort
authors.forename: Ian
authors.initials: I
authors.lastname: Fasel
authors.forename: Marian
authors.initials: M
authors.lastname: Bartlett
authors.forename: Javier
authors.initials: J
authors.lastname: Movellan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012904:Smiling; D013382:Subtraction Technique