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.} } @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.} }