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Facial Expression Recognition Using SIFT Descriptor
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 Title & Authors
Facial Expression Recognition Using SIFT Descriptor
Kim, Dong-Ju; Lee, Sang-Heon; Sohn, Myoung-Kyu;
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 Abstract
This paper proposed a facial expression recognition approach using SIFT feature and SVM classifier. The SIFT was generally employed as feature descriptor at key-points in object recognition fields. However, this paper applied the SIFT descriptor as feature vector for facial expression recognition. In this paper, the facial feature was extracted by applying SIFT descriptor at each sub-block image without key-point detection procedure, and the facial expression recognition was performed using SVM classifier. The performance evaluation was carried out through comparison with binary pattern feature-based approaches such as LBP and LDP, and the CK facial expression database and the JAFFE facial expression database were used in the experiments. From the experimental results, the proposed method using SIFT descriptor showed performance improvements of 6.06% and 3.87% compared to previous approaches for CK database and JAFFE database, respectively.
 Keywords
Facial Expression Recognition;Scale Invariant Feature Transform(SIFT);Support Vector Machine(SVM);
 Language
Korean
 Cited by
 References
1.
P. Ekman and W. V. Friesen, "Constants across cultures in the face and emotion," Journal of Personality and Social Psychology, Vol.17, No.2, pp.124-129, 1971. crossref(new window)

2.
T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, "Active shape models - their training and application," Computer Vision and Image Understanding, Vol.61, pp.38-59, 1995. crossref(new window)

3.
H. T. Le and N. T. Vo, "Face alignment using active shape model and support vector machine," International Journal of Biometrics and Bioinformatics, Vol.4, No.6, pp.224-234, 2012.

4.
David G. Lowe, "Object recognition from local scale-invariant features," Proceedings of the International Conference on Computer Vision, Vol.2. pp.1150-1157, 1999.

5.
M. T. Carlos, P. B. Marcos, and B. A. Jesus, "Fused intra-bimodal face verification approach based on scaleinvariant feature transform and a vocabulary tree," Pattern Recognition Letters, Vol.36, pp.254-260, 2014. crossref(new window)

6.
C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, Vol.20, No.3, pp.273-297, 1995.

7.
T. Kanade, J. Cohn, and Y. Tian, "Comprehensive database for facial expression analysis," IEEE International Conference Automatic Face Gesture Recognition, pp.46-53, 2000.

8.
M. J. Lyons, J. Budynek, and S. Akamatsu, "Automatic classification of single facial images," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.21, No.12, pp. 357-1362, 1999.

9.
C. Shan, S. Gong, and P. W. McOwan, "Facial expression recognition based on local binary patterns: A Comprehensive study," Image and Vision Computing, Vol.27, No.6, pp.803- 816, 2009. crossref(new window)

10.
W. L. Chao, J. J. Ding, and J. Z. Liu, "Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projection," Signal Processing, Vol.117, pp.1-10, 2015. crossref(new window)

11.
T. Jabid, M. H. Kabir, and O. Chae, "Robust facial expression recognition based on local directional pattern," ETRI Journal, Vol.32, No.5, pp.784-794, 2010. crossref(new window)

12.
F. Zhong and J. Zhang, "Face recognition with enhanced local directional patterns," Neurocomputing, Vol.119, No.7, pp.375-384, 2013. crossref(new window)