Abstract
Local Binary Pattern (LBP) is a very efficient local descriptor for describing image texture. In this paper, we propose a novel face recognition technique based on wavelet transform and the least square estimator to enhance the classical LBP. First, Wavelet transform is used to decompose a given image into four kinds of frequency images from which the features of that image can be extracted. Then, the least square estimation of local difference between each image pixel and its neighborhoods is used to build the adaptive LBP. Finally, the classification accuracy is computed using a nearest neighbor classifier with Chi-square as a dissimilarity measure. Experiments conducted on three face image datasets (ORL dataset and two avatar face image datasets); show that the proposed technique performs better than traditional methods (single scale) LBP and PCA, Wavelet Local Binary Pattern (WLBP) and Adaptive Local Binary Pattern (ALBP) in terms of accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gavrilova, M.L., Yampolskiy, R.V.: Applying Biometric Principles to Avatar Recognition. In: International Conference on Cyberworlds, Singapore, pp. 179–186 (2010)
Chen, L., Wang, Y.H., Wang, Y.D., Huang, D.: Face Recognition with Statistical Local Binary Patterns. In: 8th International Conference on Machine Learning and Cybernetics, Baoding, pp. 2433–2438 (2009)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recognition 29, 51–59 (1996)
Ahonen, T., Hadid, A., Pietikainen, M.: Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 28, 2037–2041 (2006)
Yang, H., Wang, Y.D.: A LBP-based Face Recognition Method with Hamming Distance Constraint. In: 4th International Conference on Image and Graphics, Sichuan, pp. 645–649 (2007)
Mohamed, A.A., D’Souza, D., Baili, N., Yampolskiy, R.V.: Avatar Face Recognition Using Wavelet Transform and Hierarchical Multi-scale LBP. In: 10th IEEE International Conference on Machine Learning and Applications, Honolulu, Hawaii, pp. 194–199 (2011)
Garcia, C., Zikos, G., Tziritas, G.: A Wavelet-based Framework for Face Recognition. In: 5th European Conference on Computer Vision, Freiburg, Allemagne, pp. 84–92 (1998)
Mazloom, M., Ayat, S.: Combinational Method for Face Recognition: Wavelet, PCA and ANN. In: International Conference on Digital Image Computing: Techniques and Applications, Canberra, pp. 90–95 (2008)
Meng, J., Gao, Y., Wang, X., Lin, T., Zhang, J.: Face Recognition Based on Local Binary Patterns with Threshold. In: IEEE International Conference on Granular Computing, San Jose, CA, pp. 352–356 (2010)
Wang, W., Chang, F., Zhao, J., Chen, Z.: Automatic Facial Expression Recognition Using Local Binary Pattern. In: 8th World Congress on Intelligent Control and Automation, Jinan, China, pp. 6375–6378 (2010)
Liu, X., Du, M., Jin, L.: Face Features Extraction Based on Multi-scale LBP. In: 2nd International Conference on Signal Processing Systems, pp. v2 438- v2 441 (2010)
Guo, Z., Zhang, L., Zhang, D., Zhang, S.: Rotation Invariant Texture Classification Using Adaptive LBP with Directional Statistical Features. In: 17th IEEE International Conference on Image Processing, Hong Kong, pp. 285–288 (2010)
Mohamed, A.A., Gavrilova, M.L., Yampolskiy, R.V.: Artificial Face Recognition using Wavelet Adaptive LBP with Directional Statistical Features. In: 12th International Conference on Cyberworlds, Darmstadt, Germany (2012)
The ORL Database, http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
Second Life, http://www.secondlife.com
Entropia Universe, http://www.entropiauniverse.com
Mohamed, A.A., Yampolskiy, R.V.: An Improved LBP Algorithm for Avatar Face Recognition. In: 23th International Symposium on Information, Communication and Automation Technologies, Sarajevo, Bosnia & Herzegovina, pp. 1–5 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Mohamed, A., Yampolskiy, R.V. (2013). Face Recognition Based on Wavelet Transform and Adaptive Local Binary Pattern. In: Rogers, M., Seigfried-Spellar, K.C. (eds) Digital Forensics and Cyber Crime. ICDF2C 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39891-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-39891-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39890-2
Online ISBN: 978-3-642-39891-9
eBook Packages: Computer ScienceComputer Science (R0)