Recognizing visual focus of attention from head pose in natural meetings

  • Authors:

  • Sileye O. Ba;Jean-Marc Odobez

  • Affiliations:

  • Institut Dalle Molle d'Intelligence Artificielle Perceptive, Research Institute, Martigny, Switzerland and Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland;Institut Dalle Molle d'Intelligence Artificielle Perceptive, Research Institute, Martigny, Switzerland and Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
  • Year:
  • 2009

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Abstract

We address the problem of recognizing the visual focus of attention (VFOA) of meeting participants based on their head pose. To this end, the head pose observations are modeled using a Gaussian mixture model (GMM) or a hidden Markov model (HMM) whose hidden states correspond to the VFOA. The novelties of this paper are threefold. First, contrary to previous studies on the topic, in our setup, the potential VFOA of a person is not restricted to other participants only. It includes environmental targets as well (a table and a projection screen), which increases the complexity of the task, with more VFOA targets spread in the pan as well as tilt gaze space. Second, we propose a geometric model to set the GMM or HMM parameters by exploiting results from cognitive science on saccadic eye motion, which allows the prediction of the head pose given a gaze target. Third, an unsupervised parameter adaptation step not using any labeled data is proposed, which accounts for the specific gazing behavior of each participant. Using a publicly available corpus of eight meetings featuring four persons, we analyze the above methods by evaluating, through objective performance measures, the recognition of the VFOA from head pose information obtained either using a magnetic sensor device or a vision-based tracking system. The results clearly show that in such complex but realistic situations, the VFOA recognition performance is highly dependent on how well the visual targets are separated for a given meeting participant. In addition, the results show that the use of a geometric model with unsupervised adaptation achieves better results than the use of training data to set the HMM parameters.