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This paper deals with video-based face recognition and tracking from a camera mounted on a mobile robot companion. All persons must be logically identified before being authorized to interact with the robot while continuous tracking is compulsory in order to estimate the position of this person. A first contribution relates to experiments of still-image-based face recognition methods in order to check which image projection and classifier associations lead to the highest performance of the face database acquired from our robot. Our approach, based on Principal Component Analysis (PCA) and Support Vector Machines (SVM) improved by genetic algorithm optimization of the free-parameters, is found to outperform conventional appearance-based holistic classifiers (eigenface and Fisherface) which are used as benchmarks. The integration of face recognition, dedicated to the previously identified person, as intermittent features in the particle filtering framework is well-suited to this context as it facilitates the fusion of different measurement sources by positioning the particles according to face classification probabilities in the importance function. Evaluations on key-sequences acquired by the mobile robot in crowded and continuously changing indoor environments demonstrate the tracker robustness against such natural settings. The paper closes with a discussion of possible extensions.

A TUNING STRATEGY FOR FACE RECOGNITION IN ROBOTIC APPLICATION