Impact of LBP Topologies as Texture Descriptors on Ethnicity Identification

Hawkar O. Ahmed1, Mahdi M. Younis1, and Shakhawan H. Wady2

1 Department of Statistic & Computer, College of Commerce, University of Sulaimani, Iraq

2 Department of Mathematics, School of Science Education, University of Sulaimani, Iraq

DOI: https://doi.org/10.17656/jzs.10550

Abstract

Many ethnicity identification techniques have been developed during the past years but the problem remains are the way of using these techniques, especially local binary pattern (LBP) method is one of these techniques which has shown its superiority in ethnicity identification. The original LBP operator mainly thresholds pixels in a specific predetermined window based on the gray value of the central pixel of that window. In this work, we comparativelystudy five different configuration neighborhood topology including circle, ellipse, parabola, hyperbola, and Archimedean topology. In the ellipse topology we used eight number of neighborhood pixels with different angle (0o, 45o, 90o, and135o), also in the circle topology we used vary the number of neighborhood pixels P: P = 8, P=10, and P = 12. K-nearest neighbor (KNN) has been used for identification task.A series of experimentations hasbeen performed on1200 face images were obtained from a collection of some standard databases. The topology computations that provide highly accurate identification consist of circle, and ellipse topology. In addition, the experimental results also indicate that a good accuracy and demonstrate by increasing the number of neighborhood pixel the result will be increase.

Key Words:  Ethnicity identification, Local binary patterns (LBP), topology, Euclidean Distance, KNN

References

[1]Lu, X. and Jain, A.K., "Ethnicity identification from face images", Proceedings of SPIE International Symposium on Defense and Security: Biometric Technology for Human Identification, Orlando, Florida, pp114 – 123, (2004).

[2]Yang, Z., Li, M., Ai, H., “An experimental study on automatic face gender classification”, In: Proc. IEEE Int. Conf. on Pattern Recognition, pp 1099–1102 (2006).

[3]Loris, N., Alessandra, L. and Sheryl, B., “Local Binary Patterns variants as texture descriptors for medical image analysis”, Proceedings of Artificial Intelligence in Medicine, pp. 117-125, (2010).

[4]Ojala, T., Pietikainen, M., and Maenpaa, T., “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns", IEEE Trans. Pattern Anal. Mach. Intell., Vol. 24, No.7, pp 971–987, (2002).

[5]Yang, Z. and Ai, H., "Demographic classification with local binary patterns", Proceedings of the 2007 International Conference on Advances in Biometrics (ICB'07), Seoul, Korea, pp 46447, (2007).

[6]Ahonen, T., Hadid, A., and Pietikäinen, M., “Face recognition with local binary patterns”, In Proc. 8th European Conference on Computer Vision (ECCV), pp 469–481, Prague,Czech Republic, (2004).

[7]Shang, X. and Veldhuis R., “Local binary patterns as image preprocessing for grip-pattern recognition in smart gun”, In: Proceedings of the first IEEE international conference on biometric: theory, applications, and systems, pp 1-6, (2007).

[8]Anwar M. Mirza, Muhammad Hussain, Huda Almuzaini, Ghulam Muhammad,HatimAboalsamh, and George Bebis, “Gender recognition using fusion of local and global facial features”, G. Bebis et al. (Eds.), ISVC 2013, Part II, LNCS 8034, pp 493–502, (2013).

[9]Nanni, L. and Lumini, A., “A reliable method for cell phoenotype image classification”, ArtifIntell Med, Vol. 43, No.2, pp 87-97, (2008).

[10]Ojala, T., Pietikainen, M., and Harwood, D., “A comparative study of texture measures with classification based on feature distributions”, PatternRecognition, Vol.29, pp 51–59, (1996).

[11]Pietikäinen, M., Ojala, T., and Xu.Z., “Rotation-Invariant Texture Classification Using Feature Distributions”, Pattern Recognition pp 43-52, (2000).

[12]A.O. Fatade, A.A. Arigbabu and D.C.J. Wessels, “Teaching Conic Sections and Their Applications”, Journal of Modern Mathematics and Statistics, Vol.5, Issue.3, pp 60-65, (2011).

[13]C.Dayanithi, “Combination of Cubic and Quartic Plane Curve”, IOSR Journal of Mathematics (IOSR-JM), e-ISSN: 2278-5728, p-ISSN: 2319-765X, Vol. 6, Issue 2, pp 43-53, (2013).

[14]LuwaiWazzan, “Application of Hyperbola Function Method to the Family of Third Order Korteweg-de Vries Equations”, Applied Mathematics,Vol. 6, pp 1241-1249, (2015).

[15]Girija B. Mitra, “Fourier Transforms of Tubular Objects with Spiral Structures”.Journal of Crystallization Process and Technology, Vol.2, pp 161-166, (2012).

[16]G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments”, University of Massachusetts, Amherst, Tech.Rep. pp 07-49, (October 2007).

[17]Shruthi K.C, Dr.Y.P. Gowramma, “CBR techniques with sparse codewords toRetrieve face image”, International Journal of Advanced Technology in Engineering and Science, Vol.03, Special Issue 01, (2015).

[18] Phillips, P., Moon, H., Rizvi, S., and Rauss, P., “The feret evaluation methodology for face-recognition algorithms”, IEEE Trans. PAMI, pp 1090–1104, (2010).

[19]Elisseeff, A., and Pontil, M., "Leave-one-out error and stability of learning algorithms with applications" NATO science series sub series iii computer and systems sciences Vol.190, pp 111-130, (2003).

[20]Yang M., and Moghaddam, B., “Support vector machines for visual gender classification”, in Proc. 15th Int. Pattern Recognition Conf., Vol.1, pp 1115–1118, (2000).

[21]Boiman, O., Shechtman, E., and Irani, M., “In defense of Nearest-Neighbor based image classification,” in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference, (2008).

[22]Fu, X. and Wei, W., “Centralized binary patterns embedded with image Euclidean distance for facial expression recognition”, in Proc. Int. Conf. Neural Comput., pp IV: 115–119, (2008).

View Full Article