We present the Computer Expression Recognition Toolbox (CERT), a software tool for fully automatic real-time facial expression recognition, and officially release it for free academic use. CERT can automatically code the intensity of 19 different facial actions from the Facial Action Unit Coding System (FACS) and 6 different prototypical facial expressions. It also estimates the locations of 10 facial features as well as the 3-D orientation (yaw, pitch, roll) of the head. On a database of posed facial expressions, Extended Cohn-Kanade (CK+[1]), CERT achieves an average recognition performance (probability of correctness on a two-alternative forced choice (2AFC) task between one positive and one negative example) of 90.1% when analyzing facial actions. On a spontaneous facial expression dataset, CERT achieves an accuracy of nearly 80%. In a standard dual core laptop, CERT can process 320 × 240 video images in real time at approximately 10 frames per second.
10 a3D orientation 10 aAccuracy 10 aautomatic real-time facial expression recognition 10 aCERT 10 acomputer expression recognition toolbox 10 aDetectors 10 adual core laptop 10 aEmotion recognition 10 aEncoding 10 aextended Cohn-Kanade 10 aFace 10 aface recognition 10 afacial action unit coding system 10 afacial expression dataset 10 aFacial features 10 aFACS 10 aGold 10 aImage coding 10 asoftware tool 10 asoftware tools 10 atwo-alternative forced choice task 1 aLittlewort, G. 1 aWhitehill, J. 1 aWu, T. 1 aFasel, I. 1 aFrank, M. 1 aMovellan, J. 1 aBartlett, M. uhttps://rubi.ucsd.edu/content/computer-expression-recognition-toolbox-cert 03033nas a2200565 4500008004100000022001400041245003700055210003700092260001200129300001400141490000700155520134500162653001501507653002801522653001401550653005301564653003501617653001301652653002401665653002001689653002201709653000901731653003301740653002101773653002701794653004801821653002801869653002001897653002201917653002501939653004401964653003902008653003202047653002802079653001302107653002102120653003002141653003102171653003202202653001202234653002602246653002202272653002102294100001802315700001902333700001402352700001702366700001702383856006702400 2009 eng d a0162-8828 00 aToward Practical Smile Detection 0 aToward Practical Smile Detection c11/2009 a2106-2111 0 v31 3 aMachine 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.
10 aAlgorithms 10 aArtificial intelligence 10 aAutomated 10 aautomatic facial expression recognition research 10 aBiological Pattern Recognition 10 aBiometry 10 aComputer simulation 10 aComputer vision 10 aComputer-Assisted 10 aFace 10 aFace and gesture recognition 10 aface recognition 10 afeature representation 10 ahuman-level expression recognition accuracy 10 aillumination conditions 10 aImage databases 10 aImage Enhancement 10 aImage Interpretation 10 aimage registration image representation 10 alearning (artificial intelligence) 10 amachine learning approaches 10 aMachine Learning Models 10 an Humans 10 aobject detection 10 apractical smile detection 10 aReproducibility of Results 10 aSensitivity and Specificity 10 aSmiling 10 aSubtraction Technique 10 atraining data set 10 avisual databases 1 aWhitehill, J. 1 aLittlewort, G. 1 aFasel, I. 1 aBartlett, M. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/toward-practical-smile-detection 02582nas a2200373 4500008004100000020002200041245007500063210006900138260003100207520141200238653002801650653003201678653002201710653001601732653001001748653001401758653002301772653002001795653001601815653002801831653002001859653002801879653002301907653001901930653002801949653002201977653003001999653001102029100001502040700001802055700001502073700001702088856010302105 2008 eng d a978-1-4244-2661-4 00 aBuilding a more effective teaching robot using apprenticeship learning 0 aBuilding a more effective teaching robot using apprenticeship le aMonterey, CA bIEE c08/2008 3 aWhat defines good teaching? While attributes such as timing, responsiveness to social cues, and pacing of material clearly play a role, it is difficult to create a comprehensive specification of what it means to be a good teacher. On the other hand, it is relatively easy to obtain examples of expert teaching behavior by observing a real teacher. With this inspiration as our guide, we investigated apprenticeship learning methods [1] that use data recorded from expert teachers as a means of improving the teaching abilities of RUBI, a social robot immersed in a classroom of 18-24 month old children. While this approach has achieved considerable success in mechanical control, such as automated helicopter flight [2], until now there has been little work on applying it to the field of social robotics. This paper explores two particular approaches to apprenticeship learning, and analyzes the models of teaching that each approach learns from the data of the human teacher. Empirical results indicate that the apprenticeship learning paradigm, though still nascent in its use in the social robotics field, holds promise, and that our proposed methods can already extract meaningful teaching models from demonstrations of a human expert.
10 aapprenticeship learning 10 aautomated helicopter flight 10 aAutomatic control 10 aData mining 10 aDelay 10 aeducation 10 aEducational robots 10 aexpert teaching 10 aHelicopters 10 aHuman-robot interaction 10 ahumanoid robots 10 aHumans Learning systems 10 amechanical control 10 arobot teaching 10 aRobotics and Automation 10 aRUBI social robot 10 atime 18 month to 24 month 10 atiming 1 aRuvolo, P. 1 aWhitehill, J. 1 aVirnes, M. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/building-more-effective-teaching-robot-using-apprenticeship-learning 02225nas a2200373 4500008004100000020002200041245006700063210006300130260002900193520104300222653001901265653003001284653003801314653002801352653001901380653002101399653002201420653003801442653001101480653001901491653002001510653001701530653001301547653001901560653003001579653002001609653001501629653003601644653001901680653002001699100001801719700002501737856008901762 2008 eng d a978-1-4244-2153-4 00 aA discriminative approach to frame-by-frame head pose tracking 0 adiscriminative approach to framebyframe head pose tracking aAmsterdam bIEEE c09/2008 3 aWe present a discriminative approach to frame-by-frame head pose tracking that is robust to a wide range of illuminations and facial appearances and that is inherently immune to accuracy drift. Most previous research on head pose tracking has been validated on test datasets spanning only a small (< 20) subjects under controlled illumination conditions on continuous video sequences. In contrast, the system presented in this paper was both trained and tested on a much larger database, GENKI, spanning tens of thousands of different subjects, illuminations, and geographical locations from images on the Web. Our pose estimator achieves accuracy of 5.82deg, 5.65deg, and 2.96deg root-mean-square (RMS) error for yaw, pitch, and roll, respectively. A set of 4000 images from this dataset, labeled for pose, was collected and released for use by the research community.
10 aaccuracy drift 10 acontinuous video sequence 10 acontrolled illumination condition 10 adiscriminative approach 10 aface detection 10 aface recognition 10 afacial appearance 10 aframe-by-frame head pose tracking 10 aHumans 10 aImage analysis 10 aImage databases 10 aLaboratories 10 aLighting 10 aMagnetic heads 10 amean square error methods 10 apose estimation 10 aRobustness 10 aroot-mean-square error tracking 10 aSystem testing 10 aVideo sequences 1 aWhitehill, J. 1 aMovellan, Javier, R. uhttps://rubi.ucsd.edu/content/discriminative-approach-frame-frame-head-pose-tracking