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FE8R - A Universal Method for Face Expression Recognition

Published: 14 September 2016 Publication History

Abstract

This paper proposes a new method for recognition of face expressions, called FE8R. We studied 6 standard expressions: anger, disgust, fear, happiness, sadness, surprise, and additional two: cry and natural. For experimental evaluation samples from MUG Facial Expression Database and color FERET Database were taken, with addition of cry expression. The proposed method is based on the extraction of characteristic objects from images by gradient transformation depending on the coordinates of the minimum and maximum points in each object on the face area. The gradient is ranked in degrees. Essential objects are studied in two ways: the first way incorporates slant tracking, the second is based on feature encoding using BPCC algorithm with classification by Backpropagation Artificial Neural Networks. The achieved classification rates have reached 95 %. The second method is proved to be fast and producing satisfactory results, as compared to other approaches.

References

[1]
Gu, H., Su, G., Du, C.: Feature points extraction from face. In: Proceedings of Conference on Image and Vision Computing (2003)
[2]
Zeng Z, Pantic M, Roisman GI, and Huang TS A survey of affect recognition methods: audio, visual, and spontaneous expressions IEEE Trans. Pattern Anal. Mach. Intell. 2009 31 39-58
[3]
Hedaoo SV, Katkar MD, and Khandait SP Feature tracking and expression recognition of face using dynamic Bayesian network Int. J. Eng. Trends Technol. (IJETT) 2014 8 10 517-521
[4]
Gao J, Fan L, and Li-zhong X Median null()-based method for face feature recognition Appl. Math. Comput. 2013 219 12 6410-6419
[5]
Cui Y and Fan L Feature extraction using fuzzy maximum margin criterion Neurocomputing 2012 86 52-58
[6]
Gordon, G.: Face recognition based on depth maps and surface curvature. In: SPIE Geometric Methods in Computer Vision, pp. 234–247 (1991)
[7]
Saeed K Sołdek J and Drobiazgiewicz L Object classification and recognition using toeplitz matrices Artificial Intelligence and Security in Computing Systems 2003 Massachusetts Kluwer Academic Publishers 163-172
[8]
Saeed K and Albakoor M Region growing based segmentation algorithm for typewritten and handwritten text recognition Appl. Soft Comput. 2009 9 2 608-617
[9]
Aifanti, N., Papachristou, C., Delopoulos, A.: The MUG facial expression database. In: Proceedings of the 11th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Desenzano, Italy, April 2010
[10]
Phillips PJ, Moon H, Rauss PJ, and Rizvi S The FERET evaluation methodology for face recognition algorithms IEEE Trans. Pattern Anal. Mach. Intell. 2000 22 10 1090-1104
[11]
Pantic M Li SZ and Jain A Facial expression recognition Encyclopedia of Biometrics 2009 Heidelberg Springer 400-406
[12]
Keltner D and Ekman P Lewis M and Haviland-Jones JM Facial expression of emotion Handbook of Emotions 2000 New York Guilford Press 236-249
[13]
Chen Y, Zhang S, and Zhao X Facial expression recognition via non-negative least-squares sparse coding Information 2014 5 305-331 Open Access
[14]
Lin K, Cheng W, and Li J Facial expression recognition based on geometric features and geodesic distance Int. J. Sig. Process. 2014 7 1 323-330
[15]
Kumbhar M, Jadhav A, and Patil M Facial expression recognition based on image feature Int. J. Comput. Commun. Eng. 2012 1 2 117-119
[16]
Brunelli R and Poggio T Face recognition: features versus templates IEEE Trans. Pattern Anal. Mach. Intell. 1993 15 10 1042-1052
[17]
Youssif A and Asker WAA Automatic facial expression recognition system based on geometric and appearance features Comput. Inf. Sci. 2011 4 2 115 Canadian Center of Science and Education
[18]
Bashyal S and Venayagamoorthy GK Recognition of facial expressions using Gabor wavelets and learning vector quantization J. Eng. Appl. Artif. Intell. 2008 21 1056-1064
[19]
Kumbhar M, Patil M, and Jadhav A Facial expression recognition using gabor wavelet Int. J. Comput. Appl. 2013 68 23 0975-8887
[20]
NabiZadeh N and John N Stephanidis C Automatic facial expression recognition using modified wavelet-based salient points and Gabor-wavelet filters HCI International 2013 - Posters’ Extended Abstracts 2013 Heidelberg Springer 362-366
[21]
Guo G and Dyer CR Learning from examples in the small sample case: face expression recognition IEEE Trans. Syst. Man Cybern. Part B Cybern. 2005 35 3 477-488
[22]
Gomathi V, Ramar K, and Jeevakumar AS Human facial expression recognition using MANFIS model Int. J. Electr. Electron. Eng. 2009 3 6 335-339
[23]
Gomathi V, Ramar K, and Jeevakumar AS A neuro fuzzy approach for facial expression recognition using LBP histograms J. Comput. Theory Eng. 2010 2 3 245-249
[24]
Khandait SP, Thool RC, and Khandait PD Comparative analysis of ANFIS and NN approach for expression recognition using geometry method J. Adv. Res. Comput. Sci. Softw. Eng. 2012 2 3 169-174
[25]
Albakoor, M., Albakkar, A.A., Dabsh, M., Sukkar, F.: BPCC approach for Arabic letters recognition. In: Arabnia, H.R. (ed.) IPCV, pp. 304–308. CSREA Press (2006)
[26]
Saeed K, Tabedzki M, Rybnik M, and Adamski M K3M: a universal algorithm for image skeletonization and a review of thinning techniques Int. J. Appl. Math. Comput. Sci. 2010 20 2 317-335
[27]
Mancas, M., Gosselin, B., Macq, B.: Segmentation using a region growing thresholding. In: Proceedings of the SPIE, vol. 5672, pp. 388–398 (pp. 12–13) (2005)
[28]
Tremeau A and Borel N A region growing and merging algorithm to color segmentation Pattern Recogn. 1997 30 7 1191-1203
[29]
Gottesfeld Brown L A survey of image registration techniques ACM Comput. Surv. 1992 24 325-376
[30]
Saeed K and Albakoor M A new feature extraction method for TMNN-based Arabic character classification Comput. Inform. 2007 26 4 403-420
[31]
Delac K and Grgic M Face Recognition 2007 Vienna I-Tech Education and Publishing
[32]
Canny J A computational approach to edge detection IEEE Trans. Pattern Anal. Mach. Intell. 1986 8 6 679-698
[33]
Hess, M., Martinez, M.: Facial feature extraction based on the smallest univalue segment assimilating nucleus (SUSAN) algorithm. In: Proceedings of Picture Coding Symposium (2004)
[34]
Barber CB, Dobkin DP, and Huhdanpaa H The quickhull algorithm for convex hulls ACM Trans. Math. Softw. 1996 22 4 469-483

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Published In

cover image Guide Proceedings
Computer Information Systems and Industrial Management: 15th IFIP TC8 International Conference, CISIM 2016, Vilnius, Lithuania, September 14-16, 2016, Proceedings
Sep 2016
737 pages
ISBN:978-3-319-45377-4
DOI:10.1007/978-3-319-45378-1

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 14 September 2016

Author Tags

  1. Face expression
  2. Feature extraction
  3. Feature encoding
  4. Slant tracking
  5. Artificial Neural Networks

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