Abstract
This paper primarily investigates a novel face detection method based on contourlet features. In this method, a face-pyramid is developed through contourlet transform, which includes both low and high frequency information to represent face features on multiresolutions and multidirections. The most discriminative features are then selected from the face-pyramid and are trained to construct the classifier by using the cascade boosting algorithm (Adaboost). Speed and capability are important issues for current face detection systems. This method extensively reduces feature demensions and the negative sample numbers step by step, so that the speed is increased radically. Mean-face template matching is adopted finally in the system to ensure a detection of one face in a scanned image. Extensive experiments are conducted and the results show that the proposed method is efficient in detecting frontal faces from cluttered images.
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Yang, H., Liu, Y., Sun, T., Yang, Y. (2009). A Novel Face Detection Method Based on Contourlet Features. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_27
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DOI: https://doi.org/10.1007/978-3-642-04070-2_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04069-6
Online ISBN: 978-3-642-04070-2
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