03033nas a2200565   4500008004100000022001400041245003700055210003700092260001200129300001400141490000700155520134500162653001501507653002801522653001401550653005301564653003501617653001301652653002401665653002001689653002201709653000901731653003301740653002101773653002701794653004801821653002801869653002001897653002201917653002501939653004401964653003902008653003202047653002802079653001302107653002102120653003002141653003102171653003202202653001202234653002602246653002202272653002102294100001802315700001902333700001402352700001702366700001702383856006702400       2009                        eng d  a0162-882800aToward Practical Smile Detection0 aToward Practical Smile Detection  c11/2009  a2106-21110 v313 a<p><span style="color: rgb(68, 68, 68); font-family: 'Lucida Grande', Verdana, sans-serif; font-size: 14px;">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.</span></p>
10aAlgorithms10aArtificial intelligence10aAutomated10aautomatic facial expression recognition research10aBiological Pattern Recognition10aBiometry10aComputer simulation10aComputer vision10aComputer-Assisted10aFace10aFace and gesture recognition10aface recognition10afeature representation10ahuman-level expression recognition accuracy10aillumination conditions10aImage databases10aImage Enhancement10aImage Interpretation10aimage registration image representation10alearning (artificial intelligence)10amachine learning approaches10aMachine Learning Models10an Humans10aobject detection10apractical smile detection10aReproducibility of Results10aSensitivity and Specificity10aSmiling10aSubtraction Technique10atraining data set10avisual databases1 aWhitehill, J.1 aLittlewort, G.1 aFasel, I.1 aBartlett, M.1 aMovellan, J.  uhttps://rubi.ucsd.edu/content/toward-practical-smile-detection