@inbook {77, title = {Grounding a Sociable Robot{\textquoteright}s Movements in Multimodal, Situational Engagements}, booktitle = {New Frontiers in Artificial Intelligence}, year = {2014}, pages = {267-281}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

To deal with the question of what a sociable robot is, we describe how an educational robot is encountered by children, teachers and designers in a preschool. We consider the importance of the robot{\textquoteright}s body by focusing on how its movements are contingently embedded in interactional situations. We point out that the effects of agency that these movements generate are inseparable from their grounding in locally coordinated, multimodal actions and interactions.
}, keywords = {Artificial Intelligence (incl. Robotics), Computer Appl. in Administrative Data Processing, Data Mining and Knowledge Discovery, Information Storage and Retrieval, Information Systems Applications (incl. Internet), Mathematical Logic and Formal Languages}, issn = {978-3-319-10060-9, 978-3-319-10061-6}, url = {http://link.springer.com/chapter/10.1007/978-3-319-10061-6_18}, author = {Ala{\v c}, M. and Movellan, J. and Malmir, M. and Nakano, Y. and Satoh, K. and Bekki, D.} } @conference {72, title = {Automated facial affect analysis for one-on-one tutoring applications}, booktitle = {2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011)}, year = {2011}, month = {03/2011}, publisher = {IEEE}, organization = {IEEE}, address = {Santa Barbara, CA}, abstract = {

In this paper, we explore the use of computer vision techniques to analyze students{\textquoteright} moods during one-on-one teaching interactions. The eventual goal is to create automated tutoring systems that are sensitive to the student{\textquoteright}s mood and affective state. We find that the problem of accurately determining a child{\textquoteright}s mood from a single video frame is surprisingly difficult, even for humans. However when the system is allowed to make decisions based on information from 10 to 30 seconds of video, excellent performance may be obtained.

}, keywords = {automated facial affect analysis, automated tutoring system, behavioural sciences computing, computer vision technique, Context, decision making, education, Emotion recognition, face recognition, Human, human computer interaction, Labeling, Machine Learning, Mood, n Histograms, one-on-one tutoring application, s Intelligent tutoring systems, student mood analysis}, isbn = {978-1-4244-9140-7}, author = {Butko, N. and Theocharous, G. and Philipose, M. and Movellan, J.} } @conference {73, title = {The computer expression recognition toolbox (CERT)}, booktitle = {2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011)}, year = {2011}, month = {03/2011}, publisher = {IEEE}, organization = {IEEE}, address = {Santa Barbara, CA}, abstract = {

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 {\texttimes} 240 video images in real time at approximately 10 frames per second.

}, keywords = {3D orientation, Accuracy, automatic real-time facial expression recognition, CERT, computer expression recognition toolbox, Detectors, dual core laptop, Emotion recognition, Encoding, extended Cohn-Kanade, Face, face recognition, facial action unit coding system, facial expression dataset, Facial features, FACS, Gold, Image coding, software tool, software tools, two-alternative forced choice task}, isbn = {978-1-4244-9140-7}, author = {Littlewort, G. and Whitehill, J. and Wu, T. and Fasel, I. and Frank, M. and Movellan, J. and Bartlett, M.} } @article {70, title = {Infomax Control of Eye Movements}, journal = {IEEE Transactions on Autonomous Mental Development}, volume = {2}, year = {2010}, pages = {91-107}, chapter = {91}, abstract = {

Recently, infomax methods of optimal control have begun to reshape how we think about active information gathering. We show how such methods can be used to formulate the problem of choosing where to look. We show how an optimal eye movement controller can be learned from subjective experiences of information gathering, and we explore in simulation properties of the optimal controller. This controller outperforms other eye movement strategies proposed in the literature. The learned eye movement strategies are tailored to the specific visual system of the learner-we show that agents with different kinds of eyes should follow different eye movement strategies. Then we use these insights to build an autonomous computer program that follows this approach and learns to search for faces in images faster than current state-of-the-art techniques. The context of these results is search in static scenes, but the approach extends easily, and gives further efficiency gains, to dynamic tracking tasks. A limitation of infomax methods is that they require probabilistic models of uncertainty of the sensory system, the motor system, and the external world. In the final section of this paper, we propose future avenues of research by which autonomous physical agents may use developmental experience to subjectively characterize the uncertainties they face.

}, keywords = {active information gathering, autonomous computer program, autonomous physical agent, Computer vision, dynamic tracking task, Eye movement, eye movement strategy, face detection, faces, Infomax control, motor system, object detection, optimal control, optimal eye movement controller, policy gradient, probabilistic model, sensory system, static scenes, Visual Perception, visual search, visual system}, issn = {1943-0604}, author = {Butko, N. and Movellan, J.} } @conference {64, title = {Learning to Make Facial Expressions}, booktitle = {IEEE 8th International Conference on Development and Learning, 2009. ICDL 2009}, year = {2009}, month = {06/2009}, publisher = {IEEE}, organization = {IEEE}, address = {Shanghai}, abstract = {

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.

}, keywords = {Actuators, Emotion recognition, face detection, face recognition, facial motor parameters, Feedback, Humans, learning (artificial intelligence), Machine Learning, Magnetic heads, Pediatrics, real-time facial expression recognition, Robot sensing systems, robotic head, Robots, self-guided learning, Servomechanisms, Servomotors}, isbn = {978-1-4244-4117-4}, author = {Wu, T. and Butko, N. and Ruvulo, P. and Bartlett, M. and Movellan, J.} } @article {69, title = {Toward Practical Smile Detection}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {31}, year = {2009}, month = {11/2009}, pages = {2106-2111}, chapter = {2106}, 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.

}, keywords = {Algorithms, Artificial intelligence, Automated, automatic facial expression recognition research, Biological Pattern Recognition, Biometry, Computer simulation, Computer vision, Computer-Assisted, Face, Face and gesture recognition, face recognition, feature representation, human-level expression recognition accuracy, illumination conditions, Image databases, Image Enhancement, Image Interpretation, image registration image representation, learning (artificial intelligence), machine learning approaches, Machine Learning Models, n Humans, object detection, practical smile detection, Reproducibility of Results, Sensitivity and Specificity, Smiling, Subtraction Technique, training data set, visual databases}, issn = {0162-8828}, author = {Whitehill, J. and Littlewort, G. and Fasel, I. and Bartlett, M. and Movellan, J.} } @conference {57, title = {Visual saliency model for robot cameras}, booktitle = {IEEE International Conference on Robotics and Automation, 2008. ICRA 2008}, year = {2008}, month = {05/2008}, publisher = {IEEE}, organization = {IEEE}, address = {Pasadena, CA}, abstract = {

Recent 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.

}, keywords = {Application software, approximation theory, Bayes methods, Bayesian methods, Bayesian model, camera control, Cameras, Central Processing Unit, Computational efficiency, Computational modeling, Explosions, fast approximation, human visual attention, Humans, Open loop systems, robot cameras, robot vision, Robot vision systems, robotic application, task free conditions, visual saliency model}, isbn = {978-1-4244-1646-2}, author = {Butko, N. and Zhang, L. and Cottrell, G. and Movellan, J.} } @conference {72, title = {Learning to Learn}, booktitle = {IEEE International Conference on Development and Learning}, year = {2007}, author = {Butko, N. and Movellan, J.} } @conference {45, title = {Analysis of machine learning methods for real-time recognition of facial expressions from video}, booktitle = {Computer Vision and Pattern Recognition: Face Processing Workshop}, year = {2004}, author = {Littlewort, G. and Bartlett, M. and Fasel, I. and Chenu, J. and Movellan, J.} } @article {44, title = {Towards social robots: Automatic evaluation of human-robot interaction by face detection and expression classification}, journal = {Advances in Neural Information Processing Systems}, volume = {16}, year = {2004}, pages = {1563-1570}, chapter = {1563}, author = {Littlewort, G. and Bartlett, M. and Chenu, J. and Fasel, I. and Kanda, T. and Ishiguro, H. and Movellan, J.} }