Abstract
This paper proposes a self-created multi-layer cascaded architecture for multi-view face detection. Instead of using predefined a priori about face views, the system automatically divides the face sample space using the kernel-based branching competitive learning (KBCL) network at different discriminative resolutions. To improve the detection efficiency, a coarse-to-fine search mechanism is involved in the procedure, where the boosted mirror pair of points (MPP) classifiers is employed to classify image blocks at different discriminatory levels. The boosted MPP classifiers can approximate the performance of the standard support vector machines in a hierarchical way, which allows background blocks to be excluded quickly by simple classifiers and the ‘face like’ parts remained to be judged by more complicate classifiers. Experimental results show that our system provides a high detection rate with a particularly low level of false positives.
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Yang, X., Yang, X., Xiong, H. (2010). A Novel Self-created Tree Structure Based Multi-view Face Detection. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12304-7_53
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DOI: https://doi.org/10.1007/978-3-642-12304-7_53
Publisher Name: Springer, Berlin, Heidelberg
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