Recognizing artificial faces using wavelet based adapted median binary patterns
Recognizing avatar faces is a challenge and very important issue for terrorism and security experts. Recently some avatar face recognition techniques are proposed but they are still limited. In this paper, we propose a novel face recognition technique based on discrete wavelet transform and Adapted Median Binary Pattern (AMBP) operator to recognize avatar faces from different virtual worlds. The original LBP operator mainly thresholds pixels in a specific predetermined window based on the central pixel's value of that window. As a result the LBP operator becomes more sensitive to noise especially in near-uniform or flat area regions of an image. One way to reduce the effect of noise is to update the threshold automatically based on all pixels in the neighborhood using some simple statistical operations. Experiments conducted on two virtual world avatar face image datasets show that our technique performs better than original LBP, adapted LBP, Median Binary Pattern (MBP) and wavelet statistical adapted LBP in terms of accuracy. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.