Head-and-Shoulder Detection in Varying Pose

• Published in: Advances in Natural Computation pp. 12 - 20
• Main Authors: Sun, Yi, Wang, Yan, He, Yinghao, Hua, Yong
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Face Detection; Linear Support Vector Machine; Pattern recognition. Digital image processing. Computational geometry; Support Vector Machine; Support Vector Machine Classifier; Wavelet Decomposition; Computer vision; Image analysis; Head; Statistical analysis; Image processing; Wavelet transformation; Frontal; Vector support machine; Pattern extraction; Posture
• ISBN: 9783540283256; 3540283250; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539117_3

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.