Abstract
Head-and-shoulder detection has been an important research topic in the fields of image processing and computer vision. In this paper, a head-and-shoulder detection algorithm based on wavelet decomposition technique and support vector machine (SVM) is proposed. Wavelet decomposition is used to extract features from real images, and linear SVM and non-linear SVM are trained for detection. Non-head-and-shoulder images can be removed by the linear SVM firstly, and then non-linear SVM detects head-and-shoulder images in detail. Varying head-and-shoulder pose can be detected from frontal and side views, especially from rear view. The experiment results prove that the method proposed is effective and fast to some extent.
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Sun, Y., Wang, Y., He, Y., Hua, Y. (2005). Head-and-Shoulder Detection in Varying Pose. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_3
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DOI: https://doi.org/10.1007/11539117_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
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