Please use this identifier to cite or link to this item: https://doi.org/10.1007/11744078_28
Title: Robust attentive behavior detection by non-linear head pose embedding and estimation
Authors: Hu, N.
Huang, W.
Ranganath, S. 
Issue Date: 2006
Citation: Hu, N.,Huang, W.,Ranganath, S. (2006). Robust attentive behavior detection by non-linear head pose embedding and estimation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3953 LNCS : 356-367. ScholarBank@NUS Repository. https://doi.org/10.1007/11744078_28
Abstract: We present a new scheme to robustly detect a type of human attentive behavior, which we call frequent change in focus of attention (FCFA), from video sequences. FCFA behavior can be easily perceived by people as temporal changes of human head pose (normally the pan angle). For recognition of this behavior by computer, we propose an algorithm to estimate the head pan angle in each frame of the sequence within a normal range of the head tilt angles. Developed from the ISOMAP, we learn a non-linear head pose embedding space in 2-D, which is suitable as a feature space for person-independent head pose estimation. These features are used in a mapping system to map the high dimensional head images into the 2-D feature space from which the head pan angle is calculated very simply. The non-linear person-independent mapping system is composed of two parts: 1) Radial Basis Function (RBF) interpolation, and 2) an adaptive local fitting technique. The results show that head orientation can be estimated robustly. Following the head pan angle estimation, an entropy-based classifier is used to characterize the attentive behaviors. The experimental results show that entropy of the head pan angle is a good measure, which is quite distinct for FCFA and focused attention behavior. Thus by setting an experimental threshold on the entropy value we can successfully and robustly detect FCFA behavior. © Springer-Verlag Berlin Heidelberg 2006.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/71664
ISBN: 3540338365
ISSN: 03029743
DOI: 10.1007/11744078_28
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