Conformation-based hidden markov models: Application to human face identification

IEEE Transactions on Neural Networks

Volumn 21, Issue 4, 2010, Pages 595-608

  Bouchaffra, Djamel ^a  

^a GRAMBLING STATE UNIVERSITY   (United States)

Author keywords

Dual tree wavelet transform; Embedded hidden Markov models; Face identification; Hidden Markov models (HMMs); Object contour representation; Shape decoding; Structural decoding

Indexed keywords

DUAL-TREE WAVELET TRANSFORM; EMBEDDED HIDDEN MARKOV MODEL; EMBEDDED HIDDEN MARKOV MODELS; FACE IDENTIFICATION; OBJECT CONTOUR; OBJECT CONTOUR REPRESENTATION;

DECODING; FACE RECOGNITION; FUNCTION EVALUATION; PROBABILITY DENSITY FUNCTION; VECTOR SPACES; WAVELET TRANSFORMS;

HIDDEN MARKOV MODELS;

ARTICLE; AUTOMATED PATTERN RECOGNITION; CONFORMATION; FACE; HUMAN; METHODOLOGY; PATTERN RECOGNITION; PHYSIOLOGY; PROBABILITY; PROTEIN CONFORMATION;

FACE; HUMANS; MARKOV CHAINS; MOLECULAR CONFORMATION; PATTERN RECOGNITION, AUTOMATED; PATTERN RECOGNITION, VISUAL; PROTEIN CONFORMATION;

EID: 77950864213     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2009.2039875     Document Type: Article

References (28)

1. S. Fine, Y. Singer, and N. Tishby, "The hierarchical hidden Markov models: Analysis and applications," Mach. Learn., vol.32, no.1, pp. 41-62, 1998.
2. K. Murphy and M. Paskin, "Linear time inference in hierarchical HMMs," in Proc. Neural Inf. Process. Syst., Boston, MA, Jul. 2001, pp. 833-840.
3. D. Bouchaffra and J. Tan, "Structural hidden Markov models using a relation of equivalence: Application to automotive designs," Data Mining Knowl. Disc. J., vol.12, no.1, pp. 79-96, 2006.
4. D. Bouchaffra and J. Tan, "Introduction to structural hidden Markov models: Application to handwritten numeral recognition," J. Intell. Data Ana., vol.10, no.1, pp. 67-79, 2006.
5. A. V. Nefian and M. H. Hayes, "Face recognition using an embedded HMM," in IEEE Conf. Audio Visual-Based Person Authenticat., 1999, pp. 1-6.
6. M. Brand, "Coupled hidden Markov models for modeling interactive processes," Massachusetts Inst. Technol. (MIT) Media Lab., Cambridge, MA, Tech. Rep. 405, 1997.
7. Z. Ghahramani and M. Jordan, "Factorial hidden Markov models," Mach. Learn., vol.29, no.2-3, pp. 245-273, Dec. 1997.
8. T. Kristjansson, B. Frey, and T. Huang, "Event-coupled hidden Markov models," in Proc. IEEE Int. Conf. Multimedia Exposition, 2000, vol.1, pp. 385-388.
9. Y. Bengio and P. Frasconi, "Input-output HMMs for sequence processing," IEEE Trans. Neural Netw., vol.7, no.5, pp. 1231-1249, Sep. 1996.
10. D. Bouchaffra, "Embedding HMM's-based models in a euclidean space: The topological hidden Markov models," in Proc. 19th IEEE Int. Conf. Pattern Recognit., Tampa, FL, Dec. 2008, pp. 1-4, (Oral Presentation).
11. L. Rabiner and B. Juang, Fundamentals of Speech Recognition. Englewood Cliffs, NJ: Prentice-Hall, 1993.
12. S. Eddy, "Profile hidden Markov models," Bioinformatics, vol.14, no.9, pp. 755-763, 1998.
13. K. Asai, S. Hayamizu, and H. Handa, "Prediction of protein secondary structures by hidden Markov models," Comput. Appl. Biosci., vol.9, no.2, pp. 141-146, 1993.
14. C. Lin and C. Hwang, "New forms of shape invariants from elliptic Fourier descriptors," Pattern Recognit., vol.20, no.5, pp. 535-545, 1987.
15. S.-T. Bow, Pattern Recognition and Image Preprocessing. New York: Marcel Dekker, 2002.
16. I. Biederman and G. Ju, "Surface vs. edge-based determinants of visual recognition," Cogn. Psychol., vol.20, no.1, pp. 38-64, 1988.
17. R. Bellman, "On the approximation of curves by line segments using dynamic programming," Commun. ACM, vol.4, no.6, p. 284, 1961.
18. P. M. Djuric and J. Chun, "AnMCMCsampling approach to estimation of nonstationary hidden Markov models," IEEE Trans. Signal Process., vol.50, no.5, pp. 1113-1123, May 2002.
19. A. V. Nefian and M. H. Hayes, "Maximum likelihood training of the embeddedHMMfor face detection and recognition," in Proc. IEEE Int. Conf. Image Process., 2000, pp. 33-36.
20. F. Samaria, "Face recognition using hidden Markov models," Ph.D. dissertation, Eng. Dept., Cambridge Univ., Cambridge, U.K., 1994.
21. D. Hond and L. Spacek, "Distinctive description for face processing," in Proc. 8th British Mach. Vis. Conf., Essex, U.K., 1997, pp. 320-329.
22. P. J. Phillips, H.Wechsler, J. Huang, and P. J. Rauss, "The FERET database and evaluation procedure for face recognition algorithms," Image Vis. Comput., vol.16, no.5, pp. 295-306, 1998.
23. P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, "The FERET evaluation methodology for face recognition algorithms," IEEE Trans. Pattern Aanal. Mach. Intell., vol.22, no.10, pp. 1090-1104, Oct. 2000.
24. A. V. Nefian and M. H. Hayes, "Hidden Markov models for face recognition," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process., Seattle, WA, 1998, pp. 2721-2724.
25. A. V. Nefian, "Embedded Bayesian networks for face recognition," in Proc. IEEE Int. Conf. Multimedia Expo, 2002, pp. 133-136.
26. J. Kim, J. Choi, J. Yi, and M. Turk, "Effective representation using ICA for face recognition robust to local distortion and partial occlusion," IEEE Trans. Pattern Anal. Mach. Intell., vol.27, no.12, pp. 1977-1981, Dec. 2005.
27. H. Y.Wang and X. J.Wu, "Weighted PCA space and its application in face recognition," in Proc. Int. Conf. Mach. Learn. Cybern., 2005, vol.7, pp. 4522-4527.
28. O. Ayinde and Y. H. Yang, "Face recognition based on rank correlation and Gabor filtered-images," Pattern Recognit., vol.35, no.6, pp. 1275-1289, 2002.