Open Access Open Access  Restricted Access Subscription or Fee Access

Video-Based Face Recognition Using HMM and Details Horizontal Orientation by 2D-DWT: Application to VIDTIMIT Database

(*) Corresponding author

Authors' affiliations



This paper proposes an approach based on two-dimensional discrete wavelet transformation for face recognition, based on low frequency horizontal and vertical high-frequency for a sampling horizontal of faces. This paper also suggests a model for the face recognition under large illumination variations in the videos. It shouts as well the robustness of the system with respect to the fitting variation and the luminance by the use of the histogram remapping technique in the preprocessing step. The face recognition method is based on Hidden Markov Models (HMMs) with the use of a top-to-bottom architecture. Many of these HMM’s built for each individual with robust characteristics in wide variation in illumination. Facial features are retrieved via the use of two-dimensional discrete wavelet transform in the parameterization phase. For faces images, the wavelet of Haar representation differentiates several spatial orientations. We study the application of this representation to compared data in faces recognition. This method has an accuracy of 95%. In fact, it gives the best recognition percentage if compared to any other method reported so far on VIDTIMIT, video database.
Copyright © 2016 Praise Worthy Prize - All rights reserved.


Face Detection; Face Identification; Histogram Remapping; Top-to-Bottom HMM; 2D-DWT; VIDTIMIT

Full Text:



Liu, Q., &Peng, G. Z. (2010, March). A robust skin color based face detection algorithm. In Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on (Vol. 2, pp. 525-528). IEEE.

Menser, B., & Müller, F. (1999). Face detection in color images using principal components analysis. In In Seventh Int’l Conf. on Image Processing and Its Applications.

Vinay, K. B., &Shreyas, B. S. (2006, October). Face recognition using gabor wavelets. In Signals, Systems and Computers, 2006. ACSSC'06. Fortieth Asilomar Conference on (pp. 593-597). IEEE.

Kannan, P., Shantha Selva Kumari, R., A Novel Approach for Face Recognition System Based on Rotational Invariant Transform and Artificial Neural Networks, (2014) International Review on Computers and Software (IRECOS), 9 (1), pp. 169-177.

Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1), 51-59.

Turk, M. A., &Pentland, A. P. (1991, June). Face recognition using eigenfaces. In Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91., IEEE Computer Society Conference on (pp. 586-591). IEEE.

Yu, H., & Yang, J. (2001). A direct LDA algorithm for high-dimensional data—with application to face recognition. Pattern recognition, 34(10), 2067-2070.

Haddadnia, J., Ahmadi, M., &Faez, K. (2003). An efficient feature extraction method with pseudo-Zernike moment in RBF neural network-based human face recognition system. EURASIP Journal on Advances in Signal Processing,2003(9), 1-12.

Wang, H., & Cao, Y. (2010, October). An HMM-Based Face Recognition Model under Variable Pose in Videos. In Pattern Recognition (CCPR), 2010 Chinese Conference on (pp. 1-7). IEEE.

Samaria, F. S. (1994). Face recognition using hidden Markov models (Doctoral dissertation, University of Cambridge).

Samaria, F., & Young, S. (1994). HMM-based architecture for face identification. Image and vision computing, 12(8), 537-543.

Gumus, E., Kilic, N., Sertbas, A., &Ucan, O. N. (2010). Evaluation of face recognition techniques using PCA, wavelets and SVM. Expert Systems with Applications, 37(9), 6404-6408.

Abdulameer, M., Abdullah, S., Othman, Z., Face Recognition Technique Based on Active Appearance Model, (2013) International Review on Computers and Software (IRECOS), 8 (11), pp. 2733-2739.

Khan, M. A., Xydeas, C., & Ahmed, H. (2014, September). On the application of AAM-based systems in face recognition. In Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European (pp. 2445-2449). IEEE.

Bansal, A., Mehta, K., &Arora, S. (2012, January). Face recognition using PCA and LDA algorithm. In Advanced Computing & Communication Technologies (ACCT), 2012 Second International Conference on (pp. 251-254). IEEE.

Kumar, S., Deepti, D. R., &Prabhakar, B. (2006, May). Face recognition using pseudo-2D ergodic HMM. In Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on (Vol. 2, pp. II-II). IEEE.

F. Samaria and A. Harter, (1994),Paramétrisation of stochastic model for human face identication,in Proceedings of the Second IEEE Workshop on Application of Computer Vision.1994.

Papageorgiou, C. P., Oren, M., &Poggio, T. (1998, January). A general framework for object detection. In Computer vision, 1998. Sixth international conference on (pp. 555-562). IEEE.

S. G. Mallat,July 1989,A theory for nuliresolution signal decomposition: The wavelet representation, IEEE Trans. Pattern Anal. Machine Intel, vol. 11, pp. 674–693.

Sanderson, C. (2003). Automatic person verification using speech and face information.

Jankowski, C., Kalyanswamy, A., Basson, S., & Spitz, J. (1990, April). NTIMIT: A phonetically balanced, continuous speech, telephone bandwidth speech database. In Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on (pp. 109-112). IEEE.

Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on (Vol. 1, pp. I-511). IEEE..

Štruc, V., Žibert, J., &Pavešić, N. (2009). Histogram remapping as a preprocessing step for robust face recognition, inWSEAS transactions on information science and applications, vol. 6, no. 3, pp. 520-529, 2009.

Adini, Y., Moses, Y., & Ullman, S. (1997). Face recognition: The problem of compensating for changes in illumination direction. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(7), 721-732.

Sanderson, C., &Paliwal, K. K. (2002). Likelihood normalization for face authentication in variable recording conditions. In Image Processing. 2002. Proceedings. 2002 International Conference on (Vol. 1, pp. I-301). IEEE.

Dae Young Ko, Jin Young Kim, SeongJoonBaek, (2004),A study on the implementation and robustness of face verification method under Illumination changes. Robot and Human Interactive Communication, 2004. ROMAN 2004. 13th IEEE International Workshop onIEEE,pp. 259 – 263.

Bambang, R. (2010, March). Feature Level Fusion of Speech and Face Image Based Person Identification System. In 2010 Second International Conference on Computer Engineering and Applications (pp. 221-225). IEEE.

Becker, B. C., & Ortiz, E. G. (2008, September). Evaluation of face recognition techniques for application to facebook. In Automatic Face & Gesture Recognition, 2008. FG'08. 8th IEEE International Conference on (pp. 1-6). IEEE.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize