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
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Hybrid Example-Based Single Image Super-Resolution Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Automatic Affect Analysis: From Children to Adults

verfasst von : Rizwan Ahmed Khan, Alexandre Meyer, Saida Bouakaz

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

This article presents novel and robust framework for automatic recognition of facial expressions for children. The proposed framework also achieved results better than state of the art methods for stimuli containing adult faces. The proposed framework extract features only from perceptual salient facial regions as it gets its inspiration from human visual system. In this study we are proposing novel shape descriptor, facial landmark points triangles ratio (LPTR). The framework was first tested on the “Dartmouth database of children’s faces” which contains photographs of children between 6 and 16 years of age and achieved promising results. Later we tested proposed framework on Cohn-Kanade (CK+) posed facial expression database (adult faces) and obtained results that exceeds state of the art.

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 A Gaussian Mixture Representation of Gesture Kinematics for On-Line Sign Language Video Annotation
Nächstes Kapitel A Study of Hand Motion/Posture Recognition in Two-Camera Views
Literatur
1.
Ekman, P.: Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage, 3rd edn. W. W. Norton & Company, New York (2001)
2.
Pantic, M., Pentland, A., Nijholt, A., Huang, T.: Human computing and machine understanding of human behavior: A survey. In: ACM International Conference on Multimodal Interfaces (2006)
3.
Ekman, P.: Universals and cultural differences in facial expressions of emotion. In: Nebraska Symposium on Motivation, pp. 207–283 (1971)
4.
Littlewort, G., Bartlett, M.S., Fasel, I., Susskind, J., Movellan, J.: Dynamics of facial expression extracted automatically from video. Image Vis. Comput. 24, 615–625 (2006)CrossRef
5.
Khan, R., Meyer, A., Konik, H., Bouakaz, S.: Facial expression recognition using entropy and brightness features. In: 11th International Conference on Intelligent Systems Design and Applications (2011)
6.
Tian, Y.: Evaluation of face resolution for expression analysis. Comput. Vis. Pattern Recogn. Workshop 68, 179–201 (2004)
7.
Khan, R.A., Meyer, A., Konik, H., Bouakaz, S.: Human vision inspired framework for facial expressions recognition. In: IEEE International Conference on Image Processing (2012)
8.
Zhang, Y., Ji, Q.: Active and dynamic information fusion for facial expression understanding from image sequences. IEEE Trans. Pattern Anal. Mach. Intell. 27, 699–714 (2005)CrossRef
9.
Valstar, M., Patras, I., Pantic, M.: Facial action unit detection using probabilistic actively learned support vector machines on tracked facial point data. In: IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 76–84 (2005)
10.
Khan, R., Meyer, A., Konik, H., Bouakaz, S.: Pain detection through shape and appearance features. In: 2013 IEEE International Conference on Multimedia and Expo (ICME) (2013)
11.
Bai, Y., Guo, L., Jin, L., Huang, Q.: A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. In: International Conference on Image Processing (2009)
12.
Khan, R.A., Meyer, A., Konik, H., Bouakaz, S.: Framework for reliable, real-time facial expression recognition for low resolution images. Pattern Recogn. Lett. 34, 1159–1168 (2013)CrossRef
13.
Zhaoping, L.: Theoretical understanding of the early visual processes by data compression and data selection. Netw. Comput. Neural Syst. 17, 301–334 (2006)CrossRef
14.
Khan, R.A., Meyer, A., Konik, H., Bouakaz, S.: Exploring human visual system: study to aid the development of automatic facial expression recognition framework. In: Computer Vision and Pattern Recognition Workshop (2012)
15.
Lucey, P., Cohn, J., Matthews, I., Lucey, S., Sridharan, S., Howlett, J., Prkachin, K.: Automatically detecting pain in video through facial action units. IEEE Trans. Syst., Man, Cybern., Part B: Cybern. 41, 664–674 (2011)CrossRef
16.
Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In: Fourth IEEE International Conference on Automatic face and Gesture Recognition (FG 2000), pp. 46–53 (2000)
17.
Yang, P., Liu, Q., Metaxas, D.N.: Exploring facial expressions with compositional features. In: Computer Vision Pattern Recognition, pp. 2638–2644 (2010)
18.
Ekman, P., Friesen, W.: The facial action coding system: A technique for the measurement of facial movements. Consulting Psychologist, Palo Alto (1978)
19.
Pantic, M., Valstar, M.F., Rademaker, R., Maat, L.: Web-based database for facial expression analysis. In: International Conference on Multimedia and Expo (2005)
20.
Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended cohn-kande dataset (CK+): A complete facial expression dataset for action unit and emotion-specified expression. In: Computer Vision and Pattern Recognition Workshops (2010)
21.
Dalrymple, K.A., Gomez, J., Duchaine, B.: The Dartmouth database of children’s faces: Acquisition and validation of a new face stimulus set. PLoS ONE 8, e79131 (2013)CrossRef
22.
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition (2001)
23.
Silva, C., Schnitman, L., Oliveira, L.: Detection of facial landmarks using local-based information. In: Brazilian Conference on Automation (2012)
24.
Egger, H., Pine, D., Nelson, E., Leibenluft, E., Ernst, M., K.E., T., Angold, A.: The NIMH child emotional faces picture set (NIMH-ChEFS): A new set of children’s facial emotion stimuli. Int. J. Methods Psychiatr. Res. 20(3), 145–56 (2011)
25.
Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29, 915–928 (2007)CrossRef
26.
Kotsia, I., Zafeiriou, S., Pitas, I.: Texture and shape information fusion for facial expression and facial action unit recognition. Pattern Recogn. 41, 833–851 (2008)MATHCrossRef
Metadaten
Titel
Automatic Affect Analysis: From Children to Adults
verfasst von
Rizwan Ahmed Khan
Alexandre Meyer
Saida Bouakaz
Copyright-Jahr
2015
Buch
Advances in Visual Computing

Print ISBN: 978-3-319-27862-9

Electronic ISBN: 978-3-319-27863-6

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-27863-6_28

Premium Partner

    Bildnachweise