Local Spectrum Feature Extraction for Face Recognition
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33035
Local Spectrum Feature Extraction for Face Recognition

Authors: Muhammad Imran Ahmad, Ruzelita Ngadiran, Mohd Nazrin Md Isa, Nor Ashidi Mat Isa, Mohd Zaizu Ilyas, Raja Abdullah Raja Ahmad, Said Amirul Anwar Ab Hamid, Muzammil Jusoh

Abstract:

This paper presents two techniques, local feature extraction using image spectrum and low frequency spectrum modelling using GMM to capture the underlying statistical information to improve the performance of face recognition system. Local spectrum features are extracted using overlap sub block window that are mapped on the face image. For each of this block, spatial domain is transformed to frequency domain using DFT. A low frequency coefficient is preserved by discarding high frequency coefficients by applying rectangular mask on the spectrum of the facial image. Low frequency information is non- Gaussian in the feature space and by using combination of several Gaussian functions that has different statistical properties, the best feature representation can be modelled using probability density function. The recognition process is performed using maximum likelihood value computed using pre-calculated GMM components. The method is tested using FERET datasets and is able to achieved 92% recognition rates.

Keywords: Local features modelling, face recognition system, Gaussian mixture models.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1100066

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2246

References:


[1] G. Shakhnarovich, V. Moghaddam, Face recognition in subspaces, Chapter 2, in: Handbook of Face Recognition, Springer-Verlag London Limited, 2011, pp. 19–49.
[2] A. M. Patil, S. R. Kolhe, P. M. Patil, 2D face recognition techniques: a survey, International Journal of Machine Intelligence 2 (2010) 74– 83.
[3] M. Turk, A. Pentland, Face recognition using Eigenfaces, in: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 1991, pp. 586–591.
[4] P. Belhumeur, J. Hespanha, D. Kriegman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (7) (1997) 711–720.
[5] M. I. Ahmad, W. L. Woo, S. S. Dlay, “Multimodal biometric fusion at feature level: Face and palmprint,” Proc. of International Symposium on Communication Systems Networks and Digital Signal Processing (CSNDSP), pp. 801-804, 2009.
[6] Bhat, V. S., Pujari, J. D.,"Face Recognition Using Holistic Based Approach". International Journal of Emerging Technology and Advanced Engineering, 4(7), pp. 134-141, 2014.
[7] X. He, S. Yan, Y. Hu, N. Partha, H.-J.Zhang, Face recognition using Laplacianfaces, IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (3) (2005) 328–340.
[8] M.-H. Yang, N. Ahuja, D. Kriegman. Face recognition using kernel eigenfaces, in: IEEE International Conference on Image Processing (ICIP), vol. 1, 2000, pp. 37–40.
[9] K.-I. Kim, K. Jung, H.-J.Kim, Face recognition using kernel principal component analysis, IEEE Signal Processing Letters 9 (2) (2002) 40– 42.
[10] M.-H. Yang, Kernel eigenfaces vs. kernel fisherfaces: face recognition using kernel methods, in: IEEE International Conference on Automatic Face and Gesture Recognition (AFGR), 2002, pp. 205– 211.
[11] T. Ahonen, A. Hadid, M. Pietikäinen, Face description with local binary patterns: application to face recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (12) (2006) 2037–2041.
[12] Y. Rodriguez, S. Marcel, Face authentication using adapted local binary pattern histograms, in: European Conference on Computer Vision (ECCV), 2006, pp. 321–332.
[13] F. Cardinaux, C. Sanderson, S. Marcel, Comparison of MLP and GMM classifiers for face verification on XM2VTS, in: International Conference on Audio- and Video-based Biometric Person Authentication (AVBPA), Springer, Berlin, 2003
[14] F. Samaria, S. Young, HMM-based architecture for face identification, Image and Vision Computing 12 (8) (1994) 537–543.
[15] A. Nefian, M. Hayes, Hidden Markov models for face recognition, in: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 5, 1998, pp. 2721–2724.
[16] A. Martinez, Face image retrieval using HMMs, in: IEEE Workshop on Content- Based Access of Image and Video Libraries, 1999, pp. 35–39.
[17] Hongmei Li, Dongming Zhou, RencanNie, Analysis of Face Recognition Methods in Linear Subspace, Lecture Notes in Electrical Engineering Vol.269, 2013, pp 3045-3051
[18] S. Lucey and T. Chen, "A GMM parts based face representation for improved verification through relevance adaptation," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2, 2004, pp. II855-II861.