Abstract: The present invention is a method and system for measuring human emotional response to visual stimulus, based on the person's facial expressions. Given a detected and tracked human face, it is accurately localized so that the facial features are correctly identified and localized. Face and facial features are localized using the geometrically specialized learning machines. Then the emotion-sensitive features, such as the shapes of the facial features or facial wrinkles, are extracted. The facial muscle actions are estimated using a learning machine trained on the emotion-sensitive features. The instantaneous facial muscle actions are projected to a point in affect space, using the relation between the facial muscle actions and the affective state (arousal, valence, and stance). The series of estimated emotional changes renders a trajectory in affect space, which is further analyzed in relation to the temporal changes in visual stimulus, to determine the response.

Abstract: The present invention provides a comprehensive method to design an automatic media viewership measurement system, from the problem of sensor placement for an effective sampling of the viewership to the method of extrapolating spatially sampled viewership data. The system elements that affect the viewership—site, display, crowd, and audience—are identified first. The site-viewership analysis derives some of the crucial elements in determining an effective data sampling plan: visibility, occupancy, and viewership relevancy. The viewership sampling map is computed based on the visibility map, the occupancy map, and the viewership relevancy map; the viewership measurement sensors are placed so that the sensor coverage maximizes the viewership sampling map.

Abstract: A method for accurately estimating a pose of the human head in natural scenes utilizing positions of the prominent facial features relative to the position of the head. A high-dimensional, randomly sparse representation of a human face, using a simplified facial feature model transforms a raw face image into sets of vectors representing the fits of the face to a random, sparse set of model configurations. The transformation collects salient features of the face image which are useful to estimate the pose, while suppressing irrelevant variations of face appearance. The relation between the sparse representation of the pose is learned using Support Vector Regression (SVR). The sparse representation, combined with the SVR learning is then used to estimate a pose of facial images.

Abstract: The present invention is a system and framework for automatically measuring and correlating visual characteristics of people and accumulating the data for the purpose of demographic and behavior analysis. The demographic and behavior characteristics of people are extracted from a sequence of images using techniques from computer vision. The demographic and behavior characteristics are combined with a timestamp and a location marker to provide a feature vector of a person at a particular time at a particular location. These feature vectors are then accumulated and aggregated automatically in order to generate a data set that can be statistically analyzed, data mined and/or queried.

Abstract: A method for accurately estimating a pose of the human head in natural scenes utilizing positions of the prominent facial features relative to the position of the head. A high-dimensional, randomly sparse representation of a human face, using a simplified facial feature model transforms a raw face image into sets of vectors representing the fits of the face to a random, sparse set of model configurations. The transformation collects salient features of the face image which are useful to estimate the pose, while suppressing irrelevant variations of face appearance. The relation between the sparse representation of the pose is learned using Support Vector Regression (SVR). The sparse representation, combined with the SVR learning is then used to estimate a pose of facial images.