Abstract
Many human-machine interfaces based on face gestures are strongly user-dependent. We want to overcome this limitation by using common facial features like eyes, nose and mouth for gaze recognition. In a first step an adaptive color histogram segmentation method roughly determines the region of interest including the user's face. Within this region we then use a hierarchical recognition approach to detect the facial features. Our system is based on a what-where neural network architecture and allows a fast and robust recognition rate. In the future we intend to use the conspicuous features for estimation of gaze directions.
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© 1997 Springer-Verlag Berlin Heidelberg
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Varchmin, A.C., Rae, R., Ritter, H. (1997). Facial feature detection using neural networks. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020276
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DOI: https://doi.org/10.1007/BFb0020276
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