Home // IMMM 2012, The Second International Conference on Advances in Information Mining and Management // View article
Authors:
Feifei Lee
Koji Kotani
Qiu Chen
Tadahiro Ohmi
Keywords: Face recognition; Adjacent pixel intensity difference quantization (APIDQ); Markov stationary feature (MSF); Multiresolution; Histogram feature
Abstract:
Previously, we have proposed a robust face recognition algorithm using adjacent pixel intensity difference quantization (APIDQ) histogram combined with Markov Stationary Features (MSF), so as to add spatial structure information to histogram. We named the new histogram feature as MSF-DQ feature. In this paper, we employ multi-resolution analysis for the facial image to extract more powerful personal feature. After a set of multi-resolution pyramid images is generated using sub-sampling, MSF-DQ features at different resolution levels are extracted from corresponding pyramid images. Recognition results are firstly obtained using MSF-DQ features at different resolution levels separately and then combined by weighted averaging. Publicly available AT&T database of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions, is used to evaluate the performance of the proposed algorithm. Experimental results show face recognition using proposed multi-resolution features is very efficient. The highest average recognition rate of 98.57% is obtained.
Pages: 123 to 128
Copyright: Copyright (c) IARIA, 2012
Publication date: October 21, 2012
Published in: conference
ISSN: 2326-9332
ISBN: 978-1-61208-227-1
Location: Venice, Italy
Dates: from October 21, 2012 to October 26, 2012