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
References
[1]
Z. Guo, L. Zhang, D. Zhang, and X. Mou, “Hierarchical multiscale LBP for face and palmprint recognition,” in Proceedings of the 17th IEEE International Conference on Image Processing (ICIP '10), pp. 4521–4524, Hong Kong, China, September 2010.
[2]
M. L. Gavrilova and R. V. Yampolskiy, “Applying biometric principles to avatar recognition,” in Proceedings of the 10th International Conference on Cyberworlds (CW '10), pp. 179–186, Singapore, October 2010.
[3]
J. N. Oursler, M. Price, and R. V. Yampolskiy, “Parameterized generation of avatar face dataset,” in Proceedings of the 14th International Conference on Computer Games: AI, Animation, Mobile, Interactive Multimedia, Educational & Serious Games (CGAMES '09), Louisville, KY, USA, 2009.
[4]
D. Nood and J. Attema, “The Second Life of Virtual Reality,” 2008, http://www.ecp.nl/sites/default/files/EPN_report_-The_Second_Life_of_Virtual_Reality_-_2006_October.pdf.
[5]
M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591, Maui, HI, USA, June 1991.
[6]
J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, “Face recognition using LDA-based algorithms,” IEEE Transactions on Neural Networks, vol. 14, no. 1, pp. 195–200, 2003.
[7]
T. Ahonen, A. Hadid, and M. Pietik？inen, “Face description with local binary patterns: application to face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037–2041, 2006.
[8]
L. Chen, Y.-H. Wang, Y.-D. Wang, and D. Huang, “Face recognition with statistical local binary patterns,” in Proceedings of the 8th International Conference on Machine Learning and Cybernetics, pp. 2433–2439, Baoding, China, July 2009.
[9]
J. Meng, Y. Gao, X. Wang, T. Lin, and J. Zhang, “Face recognition based on local binary patterns with threshold,” in Proceedings of the IEEE International Conference on Granular Computing (GrC '10), pp. 352–356, San Jose, CA, USA, August 2010.
[10]
H. Yang and Y. D. Wang, “A LBP-based face recognition method with hamming distance constraint,” in Proceedings of the 4th International Conference on Image and Graphics (ICIG '07), pp. 645–649, August 2007.
[11]
C.-H. Chan, J. Kittler, and K. Messer, “Multi-scale local binary pattern histograms for face recognition,” in Proceedings of the International Conference on Biometrics (ICB '07), Seoul, Korea, 2007.
[12]
Z. Guo, L. Zhang, D. Zhang, and S. Zhang, “Rotation invariant texture classification using adaptive LBP with directional statistical features,” in Proceedings of the 17th IEEE International Conference on Image Processing (ICIP '10), pp. 285–288, Hong Kong, China, September 2010.
[13]
T. Ojala, M. Pietikainen, and D. Harwood, “A comparative study of texture measures with classification based on feature distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996.
[14]
A. A. Mohamed, D. D'Souza, N. Baili, and R. V. Yampolskiy, “Avatar face recognition using Wavelet transform and hierarchical multi-scale LBP,” in Proceedings of the 10th International Conference on Machine Learning and Applications (ICMLA'11), pp. 194–199, Honolulu, Hawaii, USA, 2011.
[15]
A. A. Mohamed, M. L. Gavrilova, and R. V. Yampolskiy, “Artificial face recognition using Wavelet adaptive LBP with directional statistical features,” in Proceedings of the 12th International Conference on Cyberworlds (CW '12), Darmstadt, Germany, 2012.
[16]
M. Mazloom and S. Ayat, “Combinational method for face recognition: Wavelet, PCA and ANN,” in Proceedings of the Digital Image Computing: Techniques and Applications (DICTA '08), pp. 90–95, Canberra, Australia, December 2008.
[17]
C. Garcia, G. Zikos, and G. Tziritas, “A Wavelet-based framework for face recognition,” in Proceedings of the 5th European Conference on Computer Vision (ECCV '98), pp. 84–92, Freiburg, Germany, 1998.
[18]
W. Wang, F. Chang, J. Zhao, and Z. Chen, “Automatic facial expression recognition using local binary pattern,” in Proceedings of the 8th World Congress on Intelligent Control and Automation (WCICA '10), pp. 6375–6378, Jinan, China, July 2010.
[19]
T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.
[20]
B. S. Manjunath and W. Y. Ma, “Texture features for browsing and retrieval of image data,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 837–842, 1996.
[21]
A. A. Mohamed and R. V. Yampolskiy, “Face recognition based on Wavelet transform and adaptive local binary pattern,” in Proceedings of the 4th International Conference on Digital Forensics & Cyber Crime (ICDF2C '12), Lafayetta, Indiana, USA, 2012.
[22]
Y. Raja and S. Gong, “Sparse multiscale local binary patterns,” in Proceedings of the 17th British Machine Vision Conference, Edinburgh, UK, 2006.
