Abstract
We propose a (near) real-time face detector using a cascade of neural network (NN) ensembles for enhanced detection accuracy and efficiency. First, we form a coordinated NN ensemble by sequentially training a set of neural networks with the same topology. The training implicitly partitions the face space into a number of disjoint regions, and each NN is specialized in a specific sub-region. Second, to reduce the total computation cost for the face detection, a series of NN ensembles are cascaded by increasing complexity of base networks. Simpler NN ensembles are used at earlier stages in the cascade, which are able to reject a majority of non-face patterns in the backgrounds. Our proposed approach achieves up to 94% detection rate on the CMU+MIT test set, a 98% detection rate on a set of video sequences and 3-4 frames/sec. detection speed on a normal PC (P-IV, 3.0GHz).
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© 2005 Springer-Verlag Berlin Heidelberg
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Zuo, F., de With, P.H.N. (2005). Fast Face Detection Using a Cascade of Neural Network Ensembles. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_4
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DOI: https://doi.org/10.1007/11558484_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29032-2
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