Abstract
In this paper we present a method to recognize human faces based on histograms of local orientation. Orientation histograms were used as input feature vectors for a k-nearest neigbour classifier. We present a method to calculate orientation histograms of n×n subimages partitioning the 2D-camera image with the segmented face. Numerical experiments have been made utilizing the Olivetti Research Laboratory (ORL) database containing 400 images of 40 subjects. Remarkable recognition rates of 98% to 99% were achieved with this extremely simple approach.
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Schwenker, F., Sachs, A., Palm, G., Kestler, H.A. (2006). Orientation Histograms for Face Recognition. In: Schwenker, F., Marinai, S. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2006. Lecture Notes in Computer Science(), vol 4087. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11829898_23
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DOI: https://doi.org/10.1007/11829898_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37951-5
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