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