Wavelet-Local binary pattern based face recognition

Azad Abdullah Ameen; Hardi M. M-Saleh; Zrar Kh. Abdul

doi:10.24297/ijct.v16i1.5779

Authors

Azad Abdullah Ameen Charmo University, College of Basic Education, Computer Department
Hardi M. M-Saleh Charmo University, College of Basic Education, Computer Department
Zrar Kh. Abdul Charmo University, College of Basic Education, Computer Department

DOI:

https://doi.org/10.24297/ijct.v16i1.5779

Keywords:

Face recognition, Local Binary Pattern, wavelet transform, support vector machine, K nearest number

Abstract

Over the last twenty years face recognition has made immense progress based on statistical learning or subspace discriminant analysis. This paper investigates a technique to reduce features necessary for face recognition based on local binary pattern, which is constructed by applying wavelet transform into local binary pattern. The approach is evaluated in two ways: wavelet transform applied to the LBP features and wavelet transform applied twice on the original image and LBP features. The resultant data are compared to the results obtained without applying wavelet transform, revealing that the reduction base one wavelet achieves the same or sometimes improved accuracy. The proposed algorithm is experimented on the Cambridge ORL Face database.

Downloads

Download data is not yet available.

References

1. Zhang, G. H. (2005). Boosting local binary pattern (LBP)-based face recognition. In Advances in Biometric Person Authentication (pp. 179-186). springer.
2. Ahonen, T. H. (2006). Face description with local binary patterns: Application to face recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on , 28 (12), 2037-2041.
3. Ahonen, T. H. (2006). Face description with local binary patterns: Application to face recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on , 28 (12), 2037-2041.
4. Burrus, C. S. (1997). Introduction to wavelets and wavelet transforms: a primer.
5. Brin, S. (1995). Near neighbor search in large metric spaces.
6. Ekenel, H. K. (2008). Local binary pattern domain local appearance face recognition. Signal Processing, Communication and Applications Conference, 2008. SIU 2008. IEEE 16th , 1-4.
7. Daubechies, I. (1992). Ten lectures on wavelets. 61.
8. Guo, G. L. (2000). Face recognition by support vector machines. Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on (pp. 196-201). IEEE.
9. Graps, A. (1995). An introduction to wavelets. Computational Science & Engineering , 2 (2), 50-61.
10. Jain, A. K. (2005). Handbook of face recognition (Vol. 1). Springer.
11. Joachims, T. (2002). Learning to classify text using support vector machines: Methods, theory and algorithms. Kluwer Academic Publishers.
12. Lahdenoja, O. L. (2005). Reducing the feature vector length in local binary pattern based face recognition. Image Processing, 2005. ICIP 2005. IEEE International Conference on. 2, pp. II-914-17. IEEE.
13. Lajevardi, S. M. (2009). Facial expression recognition using log-Gabor filters and local binary pattern operators. Proceedings of the International Conference on Communication, Computer and Power, (pp. 349-353).
14. Nguyen, H. V. (2009). Local gabor binary pattern whitened pca: A novel approach for face recognition from single image per person. In Advances in Biometrics (pp. 269-278). Springer.
15. Petpon, A. S. (2009). Face recognition with local line binary pattern. Image and Graphics, 2009. ICIG'09. Fifth International Conference on (pp. 533-539). IEEE.
16. Scholkopf, B. S. (2001). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.