Feature selection of facial displays for detection of non verbal communication in natural conversation

Recognition of human communication has previously focused on deliberately acted emotions or in structured or artificial social contexts. This makes the result hard to apply to realistic social situations. This paper describes the recording of spontaneous human communication in a specific and common social situation: conversation between two people. The clips are then annotated by multiple observers to reduce individual variations in interpretation of social signals. Temporal and static features are generated from tracking using heuristic and algorithmic methods. Optimal features for classifying examples of spontaneous communication signals are then extracted by AdaBoost. The performance of the boosted classifier is comparable to human performance for some communication signals, even on this challenging and realistic data set

Online learning of robust facial feature trackers

This paper presents a head pose and facial feature estimation technique that works over a wide range of pose variations without a priori knowledge of the appearance of the face. Using simple LK trackers, head pose is estimated by Levenberg-Marquardt (LM) pose estimation using the feature tracking as constraints. Factored sampling and RANSAC are employed to both provide a robust pose estimate and identify tracker drift by constraining outliers in the estimation process. The system provides both a head pose estimate and the position of facial features and is capable of tracking over a wide range of head poses