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Volume 27 Issue 12
Dec.  2005
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Li Xiao-hua, Shen Lan-sun . Face Detection in Compressed Domain Based on Multi-level Gradient Energy Presentation[J]. Journal of Electronics & Information Technology, 2005, 27(12): 1909-1915.
Citation: Li Xiao-hua, Shen Lan-sun . Face Detection in Compressed Domain Based on Multi-level Gradient Energy Presentation[J]. Journal of Electronics & Information Technology, 2005, 27(12): 1909-1915.

Face Detection in Compressed Domain Based on Multi-level Gradient Energy Presentation

  • Received Date: 2004-05-21
  • Rev Recd Date: 2005-04-13
  • Publish Date: 2005-12-19
  • In this paper, a fast face detection algorithm for JPEG2000 color images is presented,which combines both color and texture information in order to find a good balance between speed and detection reliability. The algorithm is designed to work directly on the wavelet compressed domain which possesses the following characters: First of all, the multi-level gradient energy presentation of face pattern is proposed, which not only can highlight the facial parts in possible face patterns, but also can address effectively the problem of unknown size in face detection in compressed domain and therefore avoid the complex resolution transform in arbitrary ratios ; secondly, the skin-color model in YCbCr space is ameliorated to improve the reliability of skin segmentation; finally, a hierarchical detector which integrates the simply rule-based classifiers and complex neural network based classifier is designed to further improve the processing speed. Experimental results show that the proposed scheme is efficient and effective.
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  • 梁路宏,艾海舟, 徐光佑等. 人脸检测研究综述. 计算机学报, 2002, 25(5):449-458.[2]Chai D, Ngan K N. Face segmentation using skin-color map in videophone applications[J].IEEE Trans. on Circuits and Systems for Video Technology.1999, 9(4):551-564[3]Garcia C, Tziritas G. Face detection using quantized skin color regions merging and wavelet packet analysis[J].IEEE Trans. on Multimedia.1999, 1(3):264-277[4]Rowley H A, Baluja S, Kanade T. Neural network-based face detection[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.1998, 20(1):23-38[5]Osuna E, Freund R, Girosi F. Training support vector machines: an application to face detection. In: Proc IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, 1997: 130-136.[6]Wang H L, Chang S F. A highly efficient system for automatic face region detection in MPEG video[J].IEEE Trans. on Circuits and Systems for Video Technology.1997, 7(4):615-628[7]Luo H T, Eleftheriadis A. On face detection in the compressed domain. In: Proceedings of the Eighth ACM International Conference on Multimedia, California, 2000: 285-294.[8]Chua T S, Zhao Y L, Kankanhalli M S. Detection of human faces in a compressed domain for video stratification[J].The Visual Computer.2002, 18:121-133[9]Bin Y, Jain A K. A generic system for form dropout[J].IEEE Trans on Pattern Analysis and Machine Intelligence.1996, 18(11):1127-1134[10]Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Hawaii, 2001, 1: 511-518.[11]Sung K K, Poggio T. Example-based learning for view-based human face detection[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.1998, 20(1):39-51
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