Table of Contents
ISRN Machine Vision
Volume 2012 (2012), Article ID 810304, 8 pages
http://dx.doi.org/10.5402/2012/810304
Research Article

Wavelet-Based Multiscale Adaptive LBP with Directional Statistical Features for Recognizing Artificial Faces

1Computer Engineering and Computer Science, University of Louisville, Louisville, KY 40292, USA
2Department of Mathematics, Menoufia University, Shebin El-Kom, Menoufia 32511, Egypt

Received 21 August 2012; Accepted 29 September 2012

Academic Editors: E. Y. Du and D. P. Mukherjee

Copyright © 2012 Abdallah A. Mohamed and Roman V. Yampolskiy. 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.

Abstract

Recognizing avatar faces is a very important issue for the security of virtual worlds. In this paper, a novel face recognition technique based on the wavelet transform and the multiscale representation of the adaptive local binary pattern (ALBP) with directional statistical features is proposed to increase the accuracy rate of recognizing avatars in different virtual worlds. The proposed technique consists of three stages: preprocessing, feature extraction, and recognition. In the preprocessing and feature extraction stages, wavelet decomposition is used to enhance the common features of the same subject of images and the multiscale ALBP (MALBP) is used to extract representative features from each facial image. Then, in the recognition stage the wavelet MALBP (WMALBP) histogram dissimilarity with statistical features of each test image and each class model is used within the nearest neighbor classifier to improve the classification accuracy of the WMALBP. Experiments conducted on two virtual world avatar face image datasets show that our technique performs better than LBP, PCA, multiscale local binary pattern, ALBP, and ALBP with directional statistical features (ALBPF) in terms of the accuracy and the time required to classify each facial image to its subject.