%0 Journal Article
%T Wavelet-Based Multiscale Adaptive LBP with Directional Statistical Features for Recognizing Artificial Faces
%A Abdallah A. Mohamed
%A Roman V. Yampolskiy
%J ISRN Machine Vision
%D 2012
%R 10.5402/2012/810304
%X 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. 1. Introduction Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits [1]. Face recognition is one of the biometrics traits that received a great attention from many researchers during the past few decades because of its potential applications in a variety of civil and government-regulated domains. It usually involves initial image normalization, preparing an image for feature extraction by detecting the face in that image, extracting facial features from appearance or facial geometry, and finally classifying facial images based on extracted features. Face recognition, however, is not only concerned with recognizing human faces, but also with recognizing faces of nonbiological entities or avatars. To address the need for a decentralized, affordable, automatic, fast, secure, reliable, and accurate means of identity authentication for avatars, the concept of artimetrics has emerged [2, 3]. Artimetrics is a new area of study concerned with visual and behavioral recognition and identity verification of intelligent software agents, domestic and industrial robots, virtual world avatars, and other nonbiological entities [2, 3]. People often complain about the insufficient security system in the Second Life which motivates our research
%U http://www.hindawi.com/journals/isrn.machine.vision/2012/810304/