Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Neural Networks – State of Art, Brief History, Basic Models and Architecture Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

FE8R - A Universal Method for Face Expression Recognition

verfasst von : Majida Albakoor, Khalid Saeed, Mariusz Rybnik, Mohamad Dabash

Erschienen in: Computer Information Systems and Industrial Management

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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 \([-15,+35]\) 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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel An Electronic Document for Distributed Electronic Services
Nächstes Kapitel A Fault-Tolerant Authenticated Key-Conference Agreement Protocol with Forward Secrecy
Literatur
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, G.I., Huang, T.S.: A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31, 39–58 (2009)CrossRef
3.
Hedaoo, S.V., Katkar, M.D., Khandait, S.P.: Feature tracking and expression recognition of face using dynamic Bayesian network. Int. J. Eng. Trends Technol. (IJETT) 8(10), 517–521 (2014)CrossRef
4.
Gao, J., Fan, L., Li-zhong, X.: Median null(\(s_w\))-based method for face feature recognition. Appl. Math. Comput. 219(12), 6410–6419 (2013)MathSciNetMATH
5.
Cui, Y., Fan, L.: Feature extraction using fuzzy maximum margin criterion. Neurocomputing 86, 52–58 (2012)CrossRef
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.: Object classification and recognition using toeplitz matrices. In: Sołdek, J., Drobiazgiewicz, L. (eds.) Artificial Intelligence and Security in Computing Systems. The Kluwer International Series in Engineering and Computer Science, vol. 752, pp. 163–172. Kluwer Academic Publishers, Massachusetts (2003)CrossRef
8.
Saeed, K., Albakoor, M.: Region growing based segmentation algorithm for typewritten and handwritten text recognition. Appl. Soft Comput. 9(2), 608–617 (2009)CrossRef
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, P.J., Moon, H., Rauss, P.J., Rizvi, S.: The FERET evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)CrossRef
11.
Pantic, M.: Facial expression recognition. In: Li, S.Z., Jain, A. (eds.) Encyclopedia of Biometrics, pp. 400–406. Springer, Heidelberg (2009)
12.
Keltner, D., Ekman, P.: Facial expression of emotion. In: Lewis, M., Haviland-Jones, J.M. (eds.) Handbook of Emotions, pp. 236–249. Guilford Press, New York (2000)
13.
Chen, Y., Zhang, S., Zhao, X.: Facial expression recognition via non-negative least-squares sparse coding. Information 5, 305–331 (2014). Open AccessCrossRef
14.
Lin, K., Cheng, W., Li, J.: Facial expression recognition based on geometric features and geodesic distance. Int. J. Sig. Process. 7(1), 323–330 (2014)
15.
Kumbhar, M., Jadhav, A., Patil, M.: Facial expression recognition based on image feature. Int. J. Comput. Commun. Eng. 1(2), 117–119 (2012)CrossRef
16.
Brunelli, R., Poggio, T.: Face recognition: features versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 15(10), 1042–1052 (1993)CrossRef
17.
Youssif, A., Asker, W.A.A.: Automatic facial expression recognition system based on geometric and appearance features. Comput. Inf. Sci. 4(2), 115 (2011). Canadian Center of Science and Education
18.
Bashyal, S., Venayagamoorthy, G.K.: Recognition of facial expressions using Gabor wavelets and learning vector quantization. J. Eng. Appl. Artif. Intell. 21, 1056–1064 (2008)CrossRef
19.
Kumbhar, M., Patil, M., Jadhav, A.: Facial expression recognition using gabor wavelet. Int. J. Comput. Appl. 68(23), 0975–8887 (2013)
20.
NabiZadeh, N., John, N.: Automatic facial expression recognition using modified wavelet-based salient points and Gabor-wavelet filters. In: Stephanidis, C. (ed.) HCII 2013, Part I. CCIS, vol. 373, pp. 362–366. Springer, Heidelberg (2013)CrossRef
21.
Guo, G., Dyer, C.R.: Learning from examples in the small sample case: face expression recognition. IEEE Trans. Syst. Man Cybern. Part B Cybern. 35(3), 477–488 (2005)CrossRef
22.
Gomathi, V., Ramar, K., Jeevakumar, A.S.: Human facial expression recognition using MANFIS model. Int. J. Electr. Electron. Eng. 3(6), 335–339 (2009)
23.
Gomathi, V., Ramar, K., Jeevakumar, A.S.: A neuro fuzzy approach for facial expression recognition using LBP histograms. J. Comput. Theory Eng. 2(3), 245–249 (2010)CrossRef
24.
Khandait, S.P., Thool, R.C., Khandait, P.D.: Comparative analysis of ANFIS and NN approach for expression recognition using geometry method. J. Adv. Res. Comput. Sci. Softw. Eng. 2(3), 169–174 (2012)
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., Adamski, M.: K3M: a universal algorithm for image skeletonization and a review of thinning techniques. Int. J. Appl. Math. Comput. Sci. 20(2), 317–335 (2010)MATHCrossRef
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., Borel, N.: A region growing and merging algorithm to color segmentation. Pattern Recogn. 30(7), 1191–1203 (1997)CrossRef
29.
Gottesfeld Brown, L.: A survey of image registration techniques. ACM Comput. Surv. 24, 325–376 (1992)CrossRef
30.
Saeed, K., Albakoor, M.: A new feature extraction method for TMNN-based Arabic character classification. Comput. Inform. 26(4), 403–420 (2007)MATH
31.
Delac, K., Grgic, M.: Face Recognition. I-Tech Education and Publishing, Vienna (2007)CrossRef
32.
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRef
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, C.B., Dobkin, D.P., Huhdanpaa, H.: The quickhull algorithm for convex hulls. ACM Trans. Math. Softw. 22(4), 469–483 (1996)MathSciNetMATHCrossRef
Metadaten
Titel
FE8R - A Universal Method for Face Expression Recognition
verfasst von
Majida Albakoor
Khalid Saeed
Mariusz Rybnik
Mohamad Dabash
Copyright-Jahr
2016
Buch
Computer Information Systems and Industrial Management

Print ISBN: 978-3-319-45377-4

Electronic ISBN: 978-3-319-45378-1

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-45378-1_55

Premium Partner

    Bildnachweise