Journal of Applied Mathematics
Volume 2012 (2012), Article ID 643562, 10 pages
Research Article

An Improved AAM Method for Extracting Human Facial Features

1School of IoT Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
2CVSSP, University of Surrey, Guildford GU2 7XH, UK

Received 7 July 2011; Accepted 24 October 2011

Academic Editor: Wenyu Sun

Copyright © 2012 Tao Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Active appearance model is a statistically parametrical model, which is widely used to extract human facial features and recognition. However, intensity values used in original AAM cannot provide enough information for image texture, which will lead to a larger error or a failure fitting of AAM. In order to overcome these defects and improve the fitting performance of AAM model, an improved texture representation is proposed in this paper. Firstly, translation invariant wavelet transform is performed on face images and then image structure is represented using the measure which is obtained by fusing the low-frequency coefficients with edge intensity. Experimental results show that the improved algorithm can increase the accuracy of the AAM fitting and express more information for structures of edge and texture.