Attentive Behavior Detection by Non-Linear Head Pose Embedding and Mapping

In this paper, we present a new scheme to robustly detect a human attentive behavior, i.e., a frequent change in focus of attention (FCFA) from video sequences. The FCFA behavior can be easily perceived by people as temporal changes of human head pose. Here, we propose a non-linear head pose embedding and mapping algorithm to detect the pose in each frame of the sequence. Developed from ISOMAP, we learn a person-independent and non-linear embedding space (we call it a 2-D feature space) for different head poses. A non-linear interpolation mapping followed by an adaptive local fitting method is designed to map new frames into the 2-D feature space where head poses can be further obtained. An entropy classifier is then proposed on each sequence to detect the FCFA behavior. Experiments reported in this paper showed robust results.