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Proceedings Paper

Detecting faces in the wavelet compressed domain
Author(s): Xiaohua Li; LanSun Shen
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Paper Abstract

A novel technique that can implement face detection directly in the wavelet compressed domain is presented in this paper. The algorithm takes the entropy decoding and inverse quantized wavelet transform coefficients of JPEG2000 picture as input, and outputs the locations of the detected faces. The main contribution of this work is in proposing a multi-level gradient energy representation of face pattern based on wavelet compressed data, which permits pertinent high contrast facial parts, such as eyes, nose and mouth, to be highlighted in a compact mode no matter the face is big or small. A neural-network based classifier is designed to decide a gradient energy pattern as face or non-face. In contrast to the traditional spatial-domain techniques, the proposed compressed domain technique eliminates the unnecessary decompression step and thus has lower computational complexity. Moreover, compared with the previous methods based on DCT compressed domain, the proposed multi-level gradient energy presentation removes the complex spatial scaling operation in compressed domain and overcomes block quantization problem. Based on test results of a variety of pictures, the presented algorithm was found to be more efficient and effective than the previous related methods.

Paper Details

Date Published: 24 June 2005
PDF: 7 pages
Proc. SPIE 5960, Visual Communications and Image Processing 2005, 59602F (24 June 2005); doi: 10.1117/12.631557
Show Author Affiliations
Xiaohua Li, Beijing Univ. of Technology (China)
LanSun Shen, Beijing Univ. of Technology (China)

Published in SPIE Proceedings Vol. 5960:
Visual Communications and Image Processing 2005
Shipeng Li; Fernando Pereira; Heung-Yeung Shum; Andrew G. Tescher, Editor(s)

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