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Reseach Article

DIC Structural HMM based IWAK-means to Enclosed Face Data

by Mohammed Alhanjouri, Hana Hejazi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 18 - Number 4
Year of Publication: 2011
Authors: Mohammed Alhanjouri, Hana Hejazi
10.5120/2269-2923

Mohammed Alhanjouri, Hana Hejazi . DIC Structural HMM based IWAK-means to Enclosed Face Data. International Journal of Computer Applications. 18, 4 ( March 2011), 43-50. DOI=10.5120/2269-2923

@article{ 10.5120/2269-2923,
author = { Mohammed Alhanjouri, Hana Hejazi },
title = { DIC Structural HMM based IWAK-means to Enclosed Face Data },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 18 },
number = { 4 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 43-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume18/number4/2269-2923/ },
doi = { 10.5120/2269-2923 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:05:27.846253+05:30
%A Mohammed Alhanjouri
%A Hana Hejazi
%T DIC Structural HMM based IWAK-means to Enclosed Face Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 18
%N 4
%P 43-50
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper identifies two novel techniques for face features extraction based on two different multi-resolution analysis tools; the first called curvelet transform while the second is waveatom transform. The resultant features are trained and tested via three improved hidden Markov Model (HMM) classifiers, such as: Structural HMM (SHMM), Deviance Information Criterion-Inverse Weighted Average K-mean-SHMM (DIC-IWAK-SHMM), and Enclosed Model Selection Criterion (EMC) coupled with DIC-IWAK-SHMM as the proposed methods for face recognition. A comparative studies for DIC-IWAK-SHMM approach to recognize the face ware achieved by using two type of features; one method using Waveatom features and the other method uses 2-level Curvelet features, these two methods compared with a six methods that used in previous researches. The goal of the paper is twofold; using Deviance information criterion and IWAK-means clustering algorithm based on SHMM.

References
  1. Mandal T. and Wu Q., 2008. Face Recognition using Curvelet Based PCA, 19th International Conference on Pattern Recognition (ICPR).
  2. Waveatom: http://www.wafeatom.org
  3. Rabiner L., 1989. A tutorial on hidden markov models and selected applications in speech recognition”, IEEE Proc., vol. 77, no. 2, pp: 257–286.
  4. Nefian A., Hayes M., 2000. Maximum likelihood training of the embedded HMM for face detection and recognition, Proc. Of the IEEE International Conference on Image Processing, ICIP, Vol. 1, 10-13 September 2000, Vancouver, BC, Canada, pp. 33-36.
  5. Bai L., Shen L., 2003. Combining Wavelets with HMM for Face Recognition, 23rd International Conference on Innovative Techniques and Applications of Artificial Intelligence (SGAI '03), Cambridge, UK, 13-15 December, pp. 227-234.
  6. Nefian A., 2002. Embedded Bayesian networks for face recognition, Proc. of the IEEE International Conference on Multimedia and Expo, Vol. 2, , Lusanne, Switzerland, 26-29 August 2002, pp. 133-136.
  7. Le H., Li H., 2004. Recognizing frontal face images using hidden Markov models with one training image per person, ICPR, vol. 1, p: 318–321.
  8. Bouchaffra D., Tan J., 2006. Introduction to structural hidden markov models: application to handwritten numeral recognition, Intelligent Data Analysis Journal, Vol. 10, No.1.
  9. Mandal T., Majumdar A., Wu Q., 2007. Face Recognition by Curvelet Based Feature Extraction, Proc of ICIAR, Montreal, Canada, vol. 4633, 22-24 August 2007, pp 806-817.
  10. Mandal T., Wu Q., 2008. Face Recognition using Curvelet Based PCA, Pattern Recognition, ICPR, 19th international conference, Tampa, Florida, USA.
  11. Majumdar A., Bhattacharya A., 2007. Face Recognition by Multiresolution Curvelet Transform on Bit Quantized Facial Images, International Conference on Computational Intelligence and Multimedia Applications, vol. 2, 13.
  12. Majumdar A., Ward R., 2008. Single image per person face recognition with images synthesized by non-linear approximation, 15th IEEE International Conference on Image Processing, ICIP 2008, P: 2740–2743.
  13. Majumdar A., Ward R., 2008. Multiresolution Methods in Face Recognition. Delac K., Grgic M., Bartlett M.: Recent Advances in Face Recognition, InTech, Publisher, p: 79-96
  14. Rziza M., El Aroussi M., et al, 2009. Local Curvelet Based Classification Using Linear Discriminant Analysis for Face Recognition, International Journal of Computer Science, Vol. 4, No. 1, pp. 72
  15. Xie J., 2009. Face Recognition Based on Curvelet Transform and LS-SVM, Proceedings of the International Symposium on Information Processing (ISIP’09), Huangshan, China, 21-23 August 2009, p: 140-143.
  16. Aggarwal V., Patterh M., 2009. ECG Compression using Wavelet Packet, Cosine Packet and Wave Atom Transforms, International Journal of Electronic Engineering Research, Vol. 1, No. 3, p: 259–268.
  17. Candes E., Demanet L., et al., 2006. Fast Discrete Curvelet Transforms, Technical Report, Cal Tech, March.
  18. Curvelets: A surprisingly effective non-adaptive representation for objects with edges: http://www.Curvelet.org/papers/Curve99.pdf
  19. Demanety L., Ying L., 2007. Wave atoms and sparsely of oscillatory patterns, Appl. Comput. Harm. Anal. , February, 2007.
  20. Young S., et al., The HTK Tools and Reference Manuals, version 3.4, Cambridge University Engineering Department, 2009.
  21. Bouchaffra D., Tan J., 2006. Introduction to structural hidden markov models: application to handwritten numeral recognition, Intell. Data Anal. J., Vol. 10, Number 1, 2006.
  22. Bouchaffra D., Amira A., 2008. Structural hidden Markov models for biometrics: fusion of face and fingerprint, Pattern Recognition, Vol. 41, p: 852– 867.
  23. Han J., Kamber M., Data mining: concepts and techniques, 2nd edition, Elesvier, 2006.
  24. Rama A., Tarrés F., Rurainsky J., Eisert P., 2008. 2D-3D Mixed Face Recognition Schemes, Delac K., Grgic M., Bartlett M.: Recent Advances in Face Recognition, In_Tech publisher, p: 125-148.
  25. Face images database: http://www.cl.cam.ac.uk/Research/ DTG/attarchive
  26. Description of the Collection of Facial Images: http://cswww.essex.ac.uk/ mv/allfaces/grimace.zip
  27. Yale face database: http://cvc.yale.edu
  28. Mandal T., Wu Q., Yuan Y., 2008. Curvelet based face recognition via dimension reduction, Signal Processing. Vol. 89, No. 12, pp. 2345-2353.
Index Terms

Computer Science
Information Sciences

Keywords

HMM Curvelet Waveatom Face Recognition Structural HMM