Abstract
This paper presents an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based “face” and “non-face” prototype clusters. A 2-Value metric is proposed for computing distance features between test patterns and the distribution-based face model during classification. We show empirically that the prototypes we choose for our distribution-based model, and the metric we adopt for computing distance feature vectors, are both critical for the success of our system.
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References
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© 1995 Springer-Verlag Berlin Heidelberg
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Sung, K.K., Poggio, T. (1995). Learning human face detection in cluttered scenes. In: Hlaváč, V., Šára, R. (eds) Computer Analysis of Images and Patterns. CAIP 1995. Lecture Notes in Computer Science, vol 970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60268-2_326
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DOI: https://doi.org/10.1007/3-540-60268-2_326
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Online ISBN: 978-3-540-44781-8
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