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Feature Extraction for Facial Expression Recognition based on Hybrid Face Regions
LAJEVARDI, S.M.See more information about LAJEVARDI, S.M. on SCOPUS, HUSSAIN, Z. M.See more information about HUSSAIN, Z. M. on SCOPUS
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Download PDF pdficon (2,006 KB) | Citation | Downloads: 913 | Views: 2,399

Author keywords
facial expression recognition, Gabor filters, face regions, human computer interaction, feature extraction

References keywords
recognition(19), facial(18), lajevardi(8), gabor(7), pattern(6), image(6), hussain(5), neural(4), features(4), feature(4)
Blue keywords are present in both the references section and the paper title.

About this article
This paper appears in: Advances in Electrical and Computer Engineering
Date of publication: 2009-10-26
Volume 9, Issue 3, Year 2009, On page(s): 63 - 67
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2009.03012
Web of Science Accession Number: 000271872000012

Abstract
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Facial expression recognition has numerous applications, including psychological research, improved human computer interaction, and sign language translation. A novel facial expression recognition system based on hybrid face regions (HFR) is investigated. The expression recognition system is fully automatic, and consists of the following modules: face detection, facial detection, feature extraction, optimal features selection, and classification. The features are extracted from both whole face image and face regions (eyes and mouth) using log Gabor filters. Then, the most discriminate features are selected based on mutual information criteria. The system can automatically recognize six expressions: anger, disgust, fear, happiness, sadness and surprise. The selected features are classified using the Naive Bayesian (NB) classifier. The proposed method has been extensively assessed using Cohn-Kanade database and JAFFE database. The experiments have highlighted the efficiency of the proposed HFR method in enhancing the classification rate.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef]


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[9] Claude, F. B., Chibelushi, C., "Facial Expression Recognition: A Brief Tutorial Overview", 2003

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[CrossRef]


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[CrossRef]


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[CrossRef]


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[16] Xie, X., and Lam, K.M., "Facial expression recognition based on shape and texture", Pattern Recognition, 42(5), pp. 1003-1011, 2009
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[17] Kotsia, I., Zafeiriou, S., and Pitas, I., "Novel multiclass classifiers based on the minimization of the within-class variance", IEEE Tran. on Neural Networks, 20(1), pp. 14-34, 2009
[CrossRef] [Web of Science Times Cited 11]


[18] Geetha, A., Ramalingam, V., Palanivel, S., Palaniappan, B., "Facial expression recognition: a real time approach", Expert Systems with Applications, 36(1), pp. 303-308, 2009
[CrossRef] [Web of Science Times Cited 14]


[19] Lajevardi, S. M., Lech, M., "Facial Expression Recognition Using Neural Networks and Log-Gabor Filters", Proceedings of Digital Image Computing: Techniques and Applications (DICTA'08), pp. 77-83, Australia, 2008
[CrossRef]


[20] Lajevardi, S. M., Lech, M., "Averaged Gabor filter features for facial expression recognition", Proceedings of Digital Image Computing: Techniques and Applications (DICTA'08), pp. 71-76, Australia, 2008
[CrossRef]


[21] Lajevardi, S. M., Lech, M., "Facial expression recognition from image sequences using optimised feature selection", 23rd International Conference on Image and Vision Computing (IVCNZ'08), pp. 1-6, New Zealand, 2008
[CrossRef] [SCOPUS Times Cited 9]


[22] Lajevardi, S. M., Hussain, Z. M., "Facial expression recognition: Gabor filters versus higher-order correlators", International Conference on Communication, Computer and Power (ICCCP'08), pp. 354-358, Oman, 2009

[23] Lajevardi, S. M., Hussain, Z. M., "Facial expression recognition using log-Gabor filters and local binary pattern operators", International Conference on Communication, Computer and Power (ICCCP'08), pp. 349-353, Oman, 2009

[24] Lajevardi, S. M., Hussain, Z. M., "Zernike moments for facial expression recognition", International Conference on Communication, Computer and Power (ICCCP'08), pp. 371-381, Oman, 2009

[25] Lajevardi, S. M., Hussain, Z. M., "Feature selection for facial expression recognition based on mutual information", IEEE-GCC'09 Conference, Kuwait, 2009

[26] Lajevardi, S. M., Hussain, Z. M., "Feature selection for facial expression recognition based on optimization algorithm", Second International Workshop on Nonlinear Dynamics and Synchronization (INDS'09), Klagenfurt, Austria, 2009



References Weight

Web of Science Citations for all references: 3,023 TCR
SCOPUS Citations for all references: 4,845 TCR

Web of Science Average Citations per reference: 111.96 ACR
SCOPUS Average Citations for per reference: 179.44 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2014-03-05 17:23 in 64 seconds.




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