|1/2019 - 7|
Circular Derivative Local Binary Pattern Feature Description for Facial Expression RecognitionTCHANGOU TOUDJEU, I. , TAPAMO, J.-R.
|View the paper record and citations in|
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,929 KB) | Citation | Downloads: 874 | Views: 1,503|
affective computing, classification, face recognition, feature extraction, image texture analysis
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
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|
| 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.
 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 51]
 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]
 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 487] [SCOPUS Times Cited 684]
 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 97]
 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 40]
 O. Lahdenoja, J. Poikonen, M. Laiho, "Towards understanding the formation of uniform local binary patterns", ISRN Mach Vis., vol. 2013, pp. 1, Jun. 2013.
 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 59]
 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 228] [SCOPUS Times Cited 284]
 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 24] [SCOPUS Times Cited 33]
 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 47]
 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 1224] [SCOPUS Times Cited 1587]
 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 35]
 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.
 S. Kasim, R. Hassan, N. H. Zaini, A. SyifaaAhmad, 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 12]
 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 101] [SCOPUS Times Cited 135]
 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 124]
 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 4052] [SCOPUS Times Cited 5372]
 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 67]
 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 63] [SCOPUS Times Cited 59]
 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 652] [SCOPUS Times Cited 798]
 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 7486] [SCOPUS Times Cited 9967]
 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]
 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 36]
 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 6]
Web of Science® Citations for all references: 14,319 TCR
SCOPUS® Citations for all references: 19,513 TCR
Web of Science® Average Citations per reference: 551 ACR
SCOPUS® Average Citations per reference: 751 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 2022-05-17 06:19 in 145 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.