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Robust Attentive Behavior Detection by Non-linear Head Pose Embedding and Estimation

  • Nan Hu
  • Weimin Huang
  • Surendra Ranganath
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)

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.

Keywords

Radial Basis Function Video Sequence Focus Attention Locally Linear Embedding Head Orientation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nan Hu
    • 1
  • Weimin Huang
    • 2
  • Surendra Ranganath
    • 3
  1. 1.Electrical & Computer EngineeringUniversity of KentuckyLexingtonUSA
  2. 2.Institute for Infocomm Research (I2R)Singapore
  3. 3.Electrical & Computer EngineeringNational University of SingaporeSingapore

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