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.
VL - 2 IS - 2 ER - TY - JOUR T1 - Toward Practical Smile Detection JF - IEEE Transactions on Pattern Analysis and Machine Intelligence Y1 - 2009 A1 - Whitehill, J. A1 - Littlewort, G. A1 - Fasel, I. A1 - Bartlett, M. A1 - Movellan, J. KW - Algorithms KW - Artificial intelligence KW - Automated KW - automatic facial expression recognition research KW - Biological Pattern Recognition KW - Biometry KW - Computer simulation KW - Computer vision KW - Computer-Assisted KW - Face KW - Face and gesture recognition KW - face recognition KW - feature representation KW - human-level expression recognition accuracy KW - illumination conditions KW - Image databases KW - Image Enhancement KW - Image Interpretation KW - image registration image representation KW - learning (artificial intelligence) KW - machine learning approaches KW - Machine Learning Models KW - n Humans KW - object detection KW - practical smile detection KW - Reproducibility of Results KW - Sensitivity and Specificity KW - Smiling KW - Subtraction Technique KW - training data set KW - visual databases AB -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.
VL - 31 IS - 11 ER -