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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2009 - 12

 HIGH-IMPACT PAPER 

Feature Extraction for Facial Expression Recognition based on Hybrid Face Regions

LAJEVARDI, S.M. See more information about LAJEVARDI, S.M. on SCOPUS See more information about LAJEVARDI, S.M. on IEEExplore See more information about LAJEVARDI, S.M. on Web of Science, HUSSAIN, Z. M. See more information about HUSSAIN, Z. M. on SCOPUS See more information about HUSSAIN, Z. M. on SCOPUS See more information about HUSSAIN, Z. M. on Web of Science
 
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Download PDF pdficon (2,006 KB) | Citation | Downloads: 1,624 | Views: 7,302

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
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
SCOPUS ID: 77954728504

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

[1] Kanade, T., Cohn, J. F., and Tian, Y., "Comprehensive database for facial expression analysis", Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, pp. 46-53, 2000

[2] Kaliouby, R. E., Robinson, P., "Real-time inference of complex mental states from facial expressions and head gestures", Conference on Computer Vision and Pattern Recognition Workshop, vol. 3, pp. 181-200, 2004

[3] Tian, Y., Kanade, T., Cohn, J. F., "Recognizing action units for facial expression analysis", IEEE Tran. on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 97-115, 2001
[CrossRef] [Web of Science Times Cited 956] [SCOPUS Times Cited 1316]


[4] Viola, P., Jones, M., "Robust real-time object detection", International Journal of Computer Vision, 57(2), pp. 137-154, 2004
[CrossRef] [Web of Science Times Cited 8161] [SCOPUS Times Cited 10889]


[5] Guyon, I., Gunn, S., Nikravesh, M., Zadeh, A., "Feature Extraction Foundations and Applications", Springer, 2006
[CrossRef]


[6] Lyons, M., Akamatsu, S., Kamachi, M., and Gyoba, J., "Coding facial expressions with Gabor wavelets", In FG'98: Proceedings of the 3rd International Conference on Face and Gesture Recognition, Washington, USA, 1998

[7] Zheng, D., Zhao, Y., Wang, J., "Features extraction using a Gabor filter family", Proceedings of the Sixth IASTED International Conference Signal and Image Processing, Hawaii, USA, 2004

[8] Rish, I., "An empirical study of the naive Bayes classifier", IJCAI Workshop on Empirical Methods in Artificial Intelligence, vol. 335, pp. 41-46, 2001

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

[10] Battiti, R., "Using mutual information for selecting features in supervised neural net learning", IEEE Trans. on Neural Networks, vol. 5, no. 4, pp. 537-550, 1994
[CrossRef] [PubMed] [Web of Science Times Cited 1657] [SCOPUS Times Cited 2076]


[11] Liu, F., Wang, Z., Wang, L., Meng, X., "Facial expression recognition using HLAC features and WPCA", Lecture Notes in Computer Science, Springer, 2005
[CrossRef] [SCOPUS Times Cited 5]


[12] Buciu, I., Kotropoulos, C., and Pitas, I., "ICA and Gabor representation for facial expression recognition", International Conference on Image Processing, vol. 2, pp. 14-17, 2003
[CrossRef]


[13] Field, D.J., "Relations between the images and the response properties of cortical cells", Jour. of the Optical Society of America, pp. 2379-2394, 1987
[CrossRef] [Web of Science Times Cited 2182] [SCOPUS Times Cited 2556]


[14] Duda, R. O., Hart, P. E., Stork, D. G., "Pattern Classification", Wiley, New York, 2001

[15] Park, S., and Kim, D., "Subtle facial expression recognition using motion magnification", Pattern Recognition Letters, 30(7), pp. 708-716, 2009
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 44]


[16] Xie, X., and Lam, K.M., "Facial expression recognition based on shape and texture", Pattern Recognition, 42(5), pp. 1003-1011, 2009
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 61]


[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 29] [SCOPUS Times Cited 33]


[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 36] [SCOPUS Times Cited 49]


[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] [SCOPUS Times Cited 34]


[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] [SCOPUS Times Cited 32]


[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 29]


[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: 13,103 TCR
SCOPUS® Citations for all references: 17,124 TCR

Web of Science® Average Citations per reference: 485 ACR
SCOPUS® Average Citations per reference: 634 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 2024-06-16 07:16 in 96 seconds.




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