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Proceedings Paper

Wavelet transform application in human face recognition
Author(s): Qiang Meng; Wiley E. Thompson; Gerald M. Flachs; Jay B. Jordan
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Paper Abstract

A wavelet transformation is introduced as a new method to extract sideview face features in human face recognition. Utilizing the wavelet transformation, a sideview profile is decomposed as high frequency and low frequency components. Signal reconstruction, autocorrelation and energy distribution are used to decide a optimal decomposition level in the wavelet transformation without losing sideview features. To evaluate the feasibility of the wavelet transformation features in human sideview face recognition, the tie statistic is used to compute the complexity of the wavelet transform features. Using wavelet transformation, the sideview data size is reduced. The reduced features have almost the same ability as the original sideview face profile data in terms of distinguishing different people. The computational expense is greatly decreased. The results of the experiments are also shown in this paper.

Paper Details

Date Published: 28 July 1997
PDF: 12 pages
Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); doi: 10.1117/12.280793
Show Author Affiliations
Qiang Meng, Optical Microwave Networks, Inc. (United States)
Wiley E. Thompson, New Mexico State Univ. (United States)
Gerald M. Flachs, New Mexico State Univ. (United States)
Jay B. Jordan, New Mexico State Univ. (United States)

Published in SPIE Proceedings Vol. 3068:
Signal Processing, Sensor Fusion, and Target Recognition VI
Ivan Kadar, Editor(s)

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