This paper explores the process of self-guided learning of realistic facial expression production by a robotic head with 31 degrees of freedom. Facial motor parameters were learned using feedback from real-time facial expression recognition from video. The experiments show that the mapping of servos to expressions was learned in under one-hour of training time. We discuss how our work may help illuminate the computational study of how infants learn to make facial expressions.
10 aActuators 10 aEmotion recognition 10 aface detection 10 aface recognition 10 afacial motor parameters 10 aFeedback 10 aHumans 10 alearning (artificial intelligence) 10 aMachine Learning 10 aMagnetic heads 10 aPediatrics 10 areal-time facial expression recognition 10 aRobot sensing systems 10 arobotic head 10 aRobots 10 aself-guided learning 10 aServomechanisms 10 aServomotors 1 aWu, T. 1 aButko, N. 1 aRuvulo, P. 1 aBartlett, M. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/learning-make-facial-expressions 01620nas a2200349 4500008004100000020002200041245006600063210006600129260003200195520052200227653001900749653001900768653002800787653003500815653001300850653003900863653001400902653002300916653002400939653001700963653001100980653003900991653002101030653000901051653002501060653001501085653001501100653003001115100001501145700001701160856009301177 2008 eng d a978-1-4244-2661-4 00 aAutomatic cry detection in early childhood education settings 0 aAutomatic cry detection in early childhood education settings aMonterey, CA bIEEE c08/2008 3 aWe present results on applying a novel machine learning approach for learning auditory moods in natural environments [1] to the problem of detecting crying episodes in preschool classrooms. The resulting system achieved levels of performance approaching that of human coders and also significantly outperformed previous approaches to this problem [2].
10 aAcoustic noise 10 aauditory moods 10 aautomatic cry detection 10 abehavioural sciences computing 10 aDeafness 10 aearly childhood education settings 10 aeducation 10 aEducational robots 10 aEmotion recognition 10 ahuman coders 10 aHumans 10 alearning (artificial intelligence) 10 aMachine Learning 10 aMood 10 apreschool classrooms 10 aPrototypes 10 aRobustness 10 aWorking environment noise 1 aRuvolo, P. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/automatic-cry-detection-early-childhood-education-settings 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 02735nas a2200409 4500008004100000020002200041245004400063210004400107260003200151520155400183653002501737653002501762653001801787653002101805653001901826653001901845653001201864653002801876653002901904653002701933653001501960653002301975653002701998653001102025653002202036653001802058653001702076653002502093653002402118653002502142653002602167100001402193700001402207700001702221700001702238856007002255 2008 eng d a978-1-4244-1646-2 00 aVisual saliency model for robot cameras 0 aVisual saliency model for robot cameras aPasadena, CA bIEEE c05/2008 3 aRecent years have seen an explosion of research on the computational modeling of human visual attention in task free conditions, i.e., given an image predict where humans are likely to look. This area of research could potentially provide general purpose mechanisms for robots to orient their cameras. One difficulty is that most current models of visual saliency are computationally very expensive and not suited to real time implementations needed for robotic applications. Here we propose a fast approximation to a Bayesian model of visual saliency recently proposed in the literature. The approximation can run in real time on current computers at very little computational cost, leaving plenty of CPU cycles for other tasks. We empirically evaluate the saliency model in the domain of controlling saccades of a camera in social robotics situations. The goal was to orient a camera as quickly as possible toward human faces. We found that this simple general purpose saliency model doubled the success rate of the camera: it captured images of people 70% of the time, when compared to a 35% success rate when the camera was controlled using an open-loop scheme. After 3 saccades (camera movements), the robot was 96% likely to capture at least one person. The results suggest that visual saliency models may provide a useful front end for camera control in robotics applications.
10 aApplication software 10 aapproximation theory 10 aBayes methods 10 aBayesian methods 10 aBayesian model 10 acamera control 10 aCameras 10 aCentral Processing Unit 10 aComputational efficiency 10 aComputational modeling 10 aExplosions 10 afast approximation 10 ahuman visual attention 10 aHumans 10 aOpen loop systems 10 arobot cameras 10 arobot vision 10 aRobot vision systems 10 arobotic application 10 atask free conditions 10 avisual saliency model 1 aButko, N. 1 aZhang, L. 1 aCottrell, G. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/visual-saliency-model-robot-cameras