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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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  1/2019 - 7

 HIGHLY CITED PAPER 

Circular Derivative Local Binary Pattern Feature Description for Facial Expression Recognition

TCHANGOU TOUDJEU, I. See more information about TCHANGOU TOUDJEU, I. on SCOPUS See more information about TCHANGOU TOUDJEU, I. on IEEExplore See more information about TCHANGOU TOUDJEU, I. on Web of Science, TAPAMO, J.-R. See more information about TAPAMO, J.-R. on SCOPUS See more information about TAPAMO, J.-R. on SCOPUS See more information about TAPAMO, J.-R. on Web of Science
 
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Download PDF pdficon (1,929 KB) | Citation | Downloads: 1,251 | Views: 2,500

Author keywords
affective computing, classification, face recognition, feature extraction, image texture analysis

References keywords
facial(19), recognition(18), local(13), binary(11), patterns(8), pattern(8), image(6), classification(6), icme(4), comput(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-02-28
Volume 19, Issue 1, Year 2019, On page(s): 51 - 56
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.01007
Web of Science Accession Number: 000459986900007
SCOPUS ID: 85064192591

Abstract
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This paper presents a novel feature extraction technique called circular derivative local binary pattern (CD-LBP) for Facial Expression Recognition (FER). Motivated by uniform local binary patterns (uLBPs) which exhibits high discriminative potential at a reduced data dimension of the original LBP feature vector, we extract CD-LBP feature descriptors as a result of binary derivatives of the circular binary patterns formed by LBPs. Seven datasets consisting of CD-LBP feature vectors are derived from the Japanese female facial expression (JAFFE) database, fed individually in a K-nearest neighbor classifier and evaluated with respect to their respective recognition rate and feature vector size. The experimental results demonstrate the relevance of the proposed feature description especially when performance metrics such as recognition accuracy and running time are considered.


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

[1] N. N. Khatri, Z. H. Shah, S. A. Patel, "Facial expression recognition: A survey," International Journal of Computer Science and Information Technologies (IJCSIT), vol. 5, pp. 149-152, 2014.

[2] X. Feng, M. Pietikinen, A. Hadid, "Facial Expression Recognition with Local Binary Patterns and Linear Programming," Pattern Recognition and Image Analysis, vol. 15, no. 2, pp. 546-548, 2005.
[CrossRef] [SCOPUS Times Cited 75]


[3] L. B. Majumder, V. K. Subramanian, "Local binary pattern based facial expression recognition using Self-organizing Map," in International Joint Conference on Neural Networks (IJCNN), pp. 2375-2382, 2014.
[CrossRef] [SCOPUS Times Cited 7]


[4] D. Huang, C. Shan, M. Ardabilian, Y. Wang, L. Chen, "Local binary patterns and its application to facial image analysis: A survey," IEEE Trans. Syst. Man. Cybern. C Appl. Rev., vol. 41, no. 6, pp. 765-781, Nov. 2011.
[CrossRef] [Web of Science Times Cited 547] [SCOPUS Times Cited 775]


[5] C. Silva, T. Bouwmans, C. Frélicot, "An eXtended center-symmetric local binary pattern for background modeling and subtraction in videos," Proc. Int. Conf. Comput. Vis. Theory Appli., pp. 395-402, 2015.
[CrossRef] [SCOPUS Times Cited 106]


[6] G. Xue, L. Song, J. Sun, and M. Wu, "Hybrid center-symmetric local pattern for dynamic background subtraction," in Proc. of IEEE International Conference on Multimedia and Expo (ICME), 2011.
[CrossRef] [SCOPUS Times Cited 47]


[7] O. Lahdenoja, J. Poikonen, M. Laiho, "Towards understanding the formation of uniform local binary patterns", ISRN Mach Vis., vol. 2013, pp. 1, Jun. 2013.
[CrossRef]


[8] I. Cohen, N. Sebe, A. Garg, M. S. Lew, T. S. Huang, "Facial expression recognition from video sequences," in Proc. of IEEE International Conference on Multimedia and Expo (ICME), pp. 121-124, 2002.
[CrossRef] [SCOPUS Times Cited 62]


[9] S. Moore, R. Bowden, "Local binary patterns for multi-view facial expression recognition," Comput. Vis. Image Understanding, vol. 115, no. 4, pp. 541-558, 2011.
[CrossRef] [Web of Science Times Cited 246] [SCOPUS Times Cited 314]


[10] X. M. Zhao, S.Q. Zhang, "A review on facial expression recognition: feature extraction and classification," IETE Technical Review, vol. 33, no. 5, pp. 505-517, 2016.
[CrossRef] [Web of Science Times Cited 35] [SCOPUS Times Cited 53]


[11] X. Feng, A. Hadid, M. Pietikinen, "A coarse-to-fine classification scheme for facial expression recognition," Proc. Int. Conf. Image Anal. Recog., pp. 668-675, 2004.
[CrossRef] [SCOPUS Times Cited 48]


[12] C. Shan, S. Gong, P. W. McOwan, "Facial expression recognition based on local binary patterns: A comprehensive study," Image Vis. Comput., vol. 27, no. 6, pp. 803-816, 2009.
[CrossRef] [Web of Science Times Cited 1398] [SCOPUS Times Cited 1842]


[13] A. Sohail, P. Bhattacharya, "Classification of facial expressions using k-nearest neighbor classifier," Proc. Vision Computer Graphics Collaboration Techniques, pp. 555-566, 2007.
[CrossRef] [SCOPUS Times Cited 46]


[14] R. Suresh, S. Audithan, G. Kannan and K. Raja, "Facial Expression Recognition System Using Local Texture Features of Contourlet Transformation," Australian Journal of Basic and Applied Sciences, vol. 10. no. 2, 2016.

[15] S. Kasim, R. Hassan, N. H. Zaini, A. Syifaa’Ahmad, A. A. Ramli, R. R. Saedudin, "A Study on Facial Expression Recognition Using Local Binary Pattern," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 5, pp. 1621-6, 26 Oct. 26, 2017.
[CrossRef] [SCOPUS Times Cited 14]


[16] Y. Chang, C. Hu, R. Feris, M. Turk, "Manifold Based Analysis of Facial Expression," J. Image and Vision Computing, vol. 24, no. 6, pp. 605-614, 2006.
[CrossRef] [Web of Science Times Cited 103] [SCOPUS Times Cited 143]


[17] S. Berretti, A. D. Bimbo, P. P. B.B. Amor, M. Daoudi, "A set of selected SIFT features for 3D facial expression recognition," Proc. 20th International Conference on Pattern Recognition, pp. 4125-4128, 2010.
[CrossRef] [SCOPUS Times Cited 135]


[18] T. Ojala, M. Pietikinen, D. Harwood, "A Comparative Study of Texture Measures with Classification Based on Feature Distributions," Pattern Recognition, vol. 29, pp. 51-59, 1996.
[CrossRef] [Web of Science Times Cited 4520] [SCOPUS Times Cited 6008]


[19] S. L. Happy, A. George, A. Routray, "A real time facial expression classification system using local binary patterns," Proc. 4th Int. Conf. Intell. Human Comput. Interaction, pp. 1-5, 2012.
[CrossRef] [SCOPUS Times Cited 93]


[20] X. Zhao, S. Zhang, "Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding," EURASIP Journal of Advances in Signal Processing, vol. 2012, no. 20, pp. 1-9, 2012.
[CrossRef] [Web of Science Times Cited 69] [SCOPUS Times Cited 65]


[21] M. J. Lyons, J. Budynek, S. Akamatsu, "Automatic Classification of Single Facial Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 12, pp. 1,357-1,362, 1999.
[CrossRef] [Web of Science Times Cited 694] [SCOPUS Times Cited 861]


[22] P. Viola, M. Jones, "Robust Real-Time Face Detection," International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, May 2004.
[CrossRef] [Web of Science Times Cited 8148] [SCOPUS Times Cited 10810]


[23] X. Feng, B. Lv, Z. Li, J. Zhang, "A novel feature extraction method for facial expression recognition," Proc. Joint Conf. Inform. Sci. Issue Adv. Intell. Syst. Res., pp. 371-375, 2006.
[CrossRef] [SCOPUS Times Cited 13]


[24] K. Meena and A. Suruliandi, "Local binary patterns and its variants for face recognition," in Recent Trends in Information Technology (ICRTIT), 2011 International Conference on, 2011, pp. 782-786.
[CrossRef] [SCOPUS Times Cited 46]


[25] Y. Wu and Q. Weigen, "Facial expression recognition based on improved deep belief networks," IAP Conference Proceedings, vol. 1864, no. 1, 2017.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 7]




References Weight

Web of Science® Citations for all references: 15,762 TCR
SCOPUS® Citations for all references: 21,570 TCR

Web of Science® Average Citations per reference: 606 ACR
SCOPUS® Average Citations per reference: 830 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 2024-04-18 11:52 in 149 seconds.




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