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:
Warmth and Affection: Exploring Thermal Sensation in the Design of Parent-Child Distant Interaction Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

CAS(ME)2: A Database of Spontaneous Macro-expressions and Micro-expressions

verfasst von : Fangbing Qu, Su-Jing Wang, Wen-Jing Yan, Xiaolan Fu

Erschienen in: Human-Computer Interaction. Novel User Experiences

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

Micro-expressions are facial expressions that are characterized by short durations, involuntary generation and low intensity, and they are regarded as unique cues revealing one’s hidden emotions. Although methods for the recognition of general facial expressions have been intensively investigated, little progress has been made in the automatic recognition of micro-expressions. To further facilitate development in this field, we present a new facial expression database, CAS(ME)2, which includes 250 spontaneous macro-expression samples and 53 micro-expression samples. The CAS(ME)2 database offers both macro-expression and micro-expression samples collected from the same participants. The emotion labels in the current database are based on a combination of Action Units (AUs), self-reports of every facial movement and the emotion types of the emotion-evoking videos to improve the validity of the labeling. Baseline evaluation was also provided. This database may provide more valid and ecological expression samples for the development of automatic facial recognition systems.

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 fNIRS as a Method to Capture the Emotional User Experience: A Feasibility Study
Nächstes Kapitel Micro-Expression Recognition for Detecting Human Emotional Changes
Fußnoten
1
The onset frame is the first to change from the baseline (usually a neutral facial expression). The apex frame is the first to reach the highest observed intensity of the facial expression, and the offset frame is the first to reach the baseline.
 
Literatur
1.
DePaulo, B.M.: Nonverbal behavior and self-presentation. Psychol. Bull. 111(2), 203 (1992)CrossRef
2.
Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17(2), 124 (1971)CrossRef
3.
Friedman, H.S., Miller-Herringer, T.: Nonverbal display of emotion in public and in private: Self-monitoring, personality, and expressive cues. J. Pers. Soc. Psychol. 61(5), 766 (1991)CrossRef
4.
North, M.S., Todorov, A., Osherson, D.N.: Inferring the preferences of others from spontaneous, low-emotional facial expressions. J. Exp. Soc. Psychol. 46(6), 1109–1113 (2010)CrossRef
5.
North, M.S., Todorov, A., Osherson, D.N.: Accuracy of inferring self-and other-preferences from spontaneous facial expressions. J. Nonverbal Behav. 36(4), 227–233 (2012)CrossRef
6.
Picard, R.W.: A. Computing, and M. Editura. MIT Press, Cambridge, MA (1997)
7.
Tong, Y., Chen, J., Ji, Q.: A unified probabilistic framework for spontaneous facial action modeling and understanding. Pattern Anal. Mach. Intell. 32(2), 258–273 (2010)CrossRef
8.
Ekman, P.: Emotions Revealed: Recognizing Faces and Feelings to Improve Communication and Emotional Life. Macmillan, New York (2007)
9.
Shreve, M., et al.: Macro-and micro-expression spotting in long videos using spatio-temporal strain. In: 2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (FG 2011). IEEE (2011)
10.
Ekman, P., Friesen, W.V.: Nonverbal leakage and clues to deception. Psychiatry 32(1), 88–106 (1969)
11.
Ekman, P.: Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage. WW Norton & Company, New York (2009). (Revised Edition)
12.
Porter, S., Ten Brinke, L.: Reading between the lies identifying concealed and falsified emotions in universal facial expressions. Psychol. Sci. 19(5), 508–514 (2008)CrossRef
13.
Rothwell, J., et al.: Silent talker: a new computer-based system for the analysis of facial cues to deception. Appl. Cogn. Psychol. 20(6), 757–777 (2006)CrossRef
14.
Lyons, M., et al.: Coding facial expressions with Gabor wavelets. In: 1998 Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition. IEEE (1998)
15.
Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. In: 2002 Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition. IEEE (2002)
16.
Gross, R., et al.: Multi-pie. Image Vis. Comput. 28(5), 807–813 (2010)CrossRef
17.
Whitehill, J., et al.: Toward practical smile detection. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2106–2111 (2009)CrossRef
18.
Bartlett, M.S., et al.: Automatic recognition of facial actions in spontaneous expressions. J. Multimedia 1(6), 22–35 (2006)CrossRef
19.
Pantic, M., et al.: Web-based database for facial expression analysis. In: 2005 IEEE International Conference on Multimedia and Expo ICME 2005. IEEE (2005)
20.
Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In: Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition. IEEE (2000)
21.
Schmidt, K.L., Cohn, J.F.: Human facial expressions as adaptations: evolutionary questions in facial expression research. Am. J. Phys. Anthropol. 116(S33), 3–24 (2001)CrossRef
22.
Lucey, P., et al.: The extended cohn-kanade dataset (CK +): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE (2010)
23.
McDuff, D., et al.: Affectiva-MIT facial expression dataset (AM-FED): naturalistic and spontaneous facial expressions collected “In-the-Wild”. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE (2013)
24.
Zhang, X., et al.: BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database. Image Vis. Comput. 32(10), 692–706 (2014)CrossRef
25.
Polikovsky, S., Kameda, Y., Ohta, Y.: Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor (2009)
26.
Wang, S.-J., et al.: Face recognition and micro-expression recognition based on discriminant tensor subspace analysis plus extreme learning machine. Neural Process. Lett. 39(1), 25–43 (2014)CrossRef
27.
Pfister, T., et al.: Recognising spontaneous facial micro-expressions. In: 2011 IEEE International Conference on Computer Vision (ICCV). IEEE (2011)
28.
Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)CrossRef
29.
Wang, S.-J., et al.: Micro-expression recognition using dynamic textures on tensor independent color space. In: Pattern Recognition (ICPR). IEEE (2014)
30.
Wang, S.-J., Yan, W.-J., Zhao, G., Fu, X., Zhou, C.-G.: Micro-expression recognition using robust principal component analysis and local spatiotemporal directional features. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014 Workshops. LNCS, vol. 8925, pp. 325–338. Springer, Heidelberg (2015)CrossRef
31.
Li, X., et al.: A spontaneous micro-expression database: Inducement, collection and baseline. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). IEEE (2013)
32.
Yan, W.-J., et al. CASME database: a dataset of spontaneous micro-expressions collected from neutralized faces. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). IEEE (2013)
33.
Yan, W.-J., et al.: CASME II: an improved spontaneous micro-expression database and the baseline evaluation. PLoS ONE 9(1), e86041 (2014)CrossRef
34.
Ekman, P., Friesen, W., Hager, J.: Facial Action Coding System: The Manual on CD-ROM Instructor’s Guide. Network Information Research Co, Salt Lake City (2002)
35.
Yan, W.-J., et al.: For micro-expression recognition: database and suggestions. Neurocomputing 136, 82–87 (2014)CrossRef
36.
Yan, W.-J., et al.: How fast are the leaked facial expressions: the duration of micro-expressions. J. Nonverbal Behav. 37(4), 217–230 (2013)CrossRef
37.
Asthana, A., et al.: Robust discriminative response map fitting with constrained local models. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2013)
Metadaten
Titel
CAS(ME)2: A Database of Spontaneous Macro-expressions and Micro-expressions
verfasst von
Fangbing Qu
Su-Jing Wang
Wen-Jing Yan
Xiaolan Fu
Copyright-Jahr
2016
Buch
Human-Computer Interaction. Novel User Experiences

Print ISBN: 978-3-319-39512-8

Electronic ISBN: 978-3-319-39513-5

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-39513-5_5

Neuer Inhalt

    Bildnachweise