Facial Communicative Signals | International Journal of Social Robotics Skip to main content
Springer Nature Link
Log in

Facial Communicative Signals

Valence Recognition in Task-Oriented Human-Robot Interaction

  • Published:
International Journal of Social Robotics Aims and scope Submit manuscript

Abstract

This paper investigates facial communicative signals (head gestures, eye gaze, and facial expressions) as nonverbal feedback in human-robot interaction. Motivated by a discussion of the literature, we suggest scenario-specific investigations due to the complex nature of these signals and present an object-teaching scenario where subjects teach the names of objects to a robot, which in turn shall term these objects correctly afterwards. The robot’s verbal answers are to elicit facial communicative signals of its interaction partners. We investigated the human ability to recognize this spontaneous facial feedback and also the performance of two automatic recognition approaches. The first one is a static approach yielding baseline results, whereas the second considers the temporal dynamics and achieved classification rates comparable to the human performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. BIelefeld Robot CompaniON.

  2. Thus, this definition of ground truth circumvents typical problems of the common approaches mentioned above: There is no dependence on the necessarily subjective impressions of human raters, subjects do not need to remember the intended meaning of their various FCS displays during an interview after the experiment, and the displayed FCSs are spontaneous and not posed on request.

  3. The start and end points of the test sequence are given by the manual annotation of the database (Sect. 3.1), as this paper focuses on the principal investigation of FCSs in the described scenario. Nevertheless, an automatic determination of these segment borders as needed for the online-classification on the robot is also possible, using the robot’s system state and simple heuristics. Approximate segment borders are sufficient, as a search for the best-matching subsequences is performed anyway. Also an incremental evaluation without a fixed end point is possible efficiently. However, the details of such a robot system are beyond the scope of this paper.

  4. However, it is not clear to which degree the humans performed a person-dependent or -independent classification. At least some adaptation to the shown people took place in the course of the experiment.

References

  1. Argyle M, Cook M (1976) Gaze and mutual gaze. Cambridge University Press, Cambridge

    Google Scholar 

  2. Argyle M, Graham JA (1976) The central Europe experiment: Looking at persons and looking at objects. J Nonverbal Behav 1(1):6–16

    Article  Google Scholar 

  3. Argyle M, Ingham R (1972) Gaze, mutual gaze, and proximity. Semiotica 6(1):32–49

    Article  Google Scholar 

  4. Bainum CK, Lounsbury KR, Pollio HR (1984) The development of laughing and smiling in nursery school children. Child Dev 55(5):1946–1957

    Article  Google Scholar 

  5. Baker S, Matthews I, Xiao J, Gross R, Kanade, T, Ishikawa, T (2004) Real-time non-rigid driver head tracking for driver mental state estimation. In: 11th World congress Intell transp syst

    Google Scholar 

  6. Barkhuysen P, Krahmer E, Swerts M (2005) Problem detection in human-machine interactions based on facial expressions of users. Speech Commun 45(3):343–359

    Article  Google Scholar 

  7. Bartlett M, Littlewort G, Frank M, Lainscsek C, Fasel I, Movellan J (2006) Fully automatic facial action recognition in spontaneous behavior. In: International conference on automatic face and gesture recognition, pp 223–230

    Chapter  Google Scholar 

  8. Bavelas JB, Black A, Lemery, CR, Mullett, J (1986) I show how you feel: Motor mimicry as a communicative act. J Pers Soc Psychol 50(2):322–329

    Article  Google Scholar 

  9. Bindemann M, Burton AM, Langton SRH (2007) How do eye gaze and facial expression interact? Vis Cogn 16(6):708–733

    Article  Google Scholar 

  10. Birdwhistell R (1970) Kinesics and context: essays on body motion communication. University of Pennsylvania Press, Philadelphia

    Google Scholar 

  11. Boucher JD, Carlson GE (1980) Recognition of facial expression in three cultures. J Cross-Cult Psychol 11:263–280

    Article  Google Scholar 

  12. Bradley MM, Lang PJ (1994) Measuring emotion: the self-assessment manikin and the semantic differential. J Behav Ther Exp Psychiatry 25(1):49–59

    Article  Google Scholar 

  13. Brightman VJ, Segal AL, Werther P, Steiner J (1975) Ethologic study of facial expressions in response to taste stimuli. J Dent Res 54:L141 (Abstract)

    Google Scholar 

  14. Brightman VJ, Segal AL, Werther P, Steiner J (1977) Facial expression and hedonic response to face stimuli. J Dent Res 56:B161 (Abstract)

    Google Scholar 

  15. Buenaposada JM, Muñoz E, Baumela L (2008) Recognising facial expressions in video sequences. PAA Pattern Anal Appl 11(1):101–116

    Article  MathSciNet  Google Scholar 

  16. Bush LE (1973) Individual differences multidimensional scaling of adjectives denoting feelings. J Pers Soc Psychol 25(1):50–57

    Article  Google Scholar 

  17. Caridakis G, Malatesta L, Kessous L, Amir N, Raouzaiou A, Karpouzis K (2006) Modeling naturalistic affective states via facial and vocal expressions recognition. In: International conference on multimodal interfaces, pp 146–154

    Google Scholar 

  18. Castrillón M, Déniz O, Hernández M (2003) The encara system for face detection and normalization. In: Lecture notes in computer science, vol 2652. Springer, Berlin, pp 176–183

    Google Scholar 

  19. Chovil N (1991) Social determinants of facial displays. J Nonverbal Behav 15(3):141–154

    Article  Google Scholar 

  20. Cline MG (1967) The perception of where a person is looking. Am J Psychol 80(1):41–50

    Article  MathSciNet  Google Scholar 

  21. Cohn J, Reed L, Ambadar Z, Xiao J, Moriyama T (2004) Automatic analysis and recognition of brow actions and head motion in spontaneous facial behavior. In: International conference on systems, man and cybernetics, pp 610–616

    Google Scholar 

  22. Cooper RM (2006) The effects of eye gaze and emotional facial expression on the allocation of visual attention. Ph.D. thesis, University of Stirling, Department of Psychology

  23. Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. Pattern Anal Mach Intell 23(6):681–685

    Article  Google Scholar 

  24. Ducci L, Arcuri L, Georgis WT, Sineshaw T (1982) Emotion recognition in Ethiopia: the effect of familiarity with western culture on accuracy of recognition. J Cross-Cult Psychol 13:340–351

    Article  Google Scholar 

  25. Edwards G, Cootes T, Taylor C (1998) Face recognition using active appearance models. In: Burkhardt H, Neumann B (eds) European conference on computer vision, vol 2. Springer, Berlin, pp 581–695

    Google Scholar 

  26. Efran JS (1968) Looking for approval: effects on visual behavior of approbation from persons differing in importance. J Pers Soc Psychol 10(1):21–25

    Article  Google Scholar 

  27. Ekman P (1971) Universals and cultural differences in facial expressions of emotion. Neb Symp Motiv 19:207–283

    Google Scholar 

  28. Ekman P (1992) An argument for basic emotions. Cogn Emot 6(3 & 4):169–200

    Article  Google Scholar 

  29. Ekman P (1994) Strong evidence for universals in facial expressions: a reply to Russell’s mistaken critique. Psychol Bull 115(2):268–287

    Article  Google Scholar 

  30. Ekman P (1997) Should we call it expression or communication? Innovation 10(4):333–344

    MathSciNet  Google Scholar 

  31. Ekman P, Friesen W (1978) Facial action coding system: a technique for the measurement of facial movement. Consulting Psychologists Press, Palo Alto

    Google Scholar 

  32. Ekman P, Davidson RJ, Friesen WV (1990) The duchenne smile: emotional expression and brain physiology ii. J Pers Soc Psychol 58(2):342–353

    Article  Google Scholar 

  33. Ekman P, Friesen WV, O’Sullivan M, Chan A, Diacoyanni-Tarlatzis I, Heider K, Krause R, LeCompte WA, Pitcairn T, Ricci-Bitti PE, Scherer K, Tomita M, Tzavaras A (1987) Universals and cultural differences in the judgments of facial expressions of emotion. J Pers Soc Psychol 53(4):712–717

    Article  Google Scholar 

  34. Ekman P, Sorenson ER, Friesen WV (1969) Pan-cultural elements in facial displays of emotion. Science 164:86–88

    Article  Google Scholar 

  35. El Kaliouby R, Robinson P (2007) Real-time inference of complex mental states from facial expressions and head gestures. Trans Robot 23(5):991–1000

    Article  Google Scholar 

  36. Emery NJ (2000) The eyes have it: the neuroethology, function and evolution of social gaze. Neurosci Biobehav Rev 24(6):581–604

    Article  Google Scholar 

  37. Exline RV (1963) Explorations in the process of person perception: visual interaction in relation to competition, sex, and need for affiliation. J Pers 31(1):1–20

    Article  Google Scholar 

  38. Exline R, Gray D, Schuette D (1965) Visual behavior in a dyad as affected by interview content and sex of respondent. J Pers Soc Psychol 1(3):201–209

    Article  Google Scholar 

  39. Exline RV, Winter LC (1965) Affect, cognition and personality, chap. Affective relations and mutual glances in dyads. Springer, New York

    Google Scholar 

  40. Fasel B, Luettin J (2003) Automatic facial expression analysis: a survey. Pattern Recognit 36:259–275

    Article  MATH  Google Scholar 

  41. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7:179–188

    Article  Google Scholar 

  42. Fragopanagos N, Taylor J (2005) Emotion recognition in human-computer interaction. Neural Netw 18(4):389–405

    Article  Google Scholar 

  43. Fridlund AJ (1991) Sociality of solitary smiling: Potentiation by an implicit audience. J Pers Soc Psychol 60(2):229–240

    Article  Google Scholar 

  44. Fridlund AJ (1994) Human facial expression: an evolutionary view. Academic Press, San Diego

    Google Scholar 

  45. Fridlund AJ, Sabini JP, Hedlund LE, Schaut JA, Shenker JI, Knauer MJ (1990) Audience effects on solitary faces during imagery: displaying to the people in your head. J Nonverbal Behav 14(2):113–137

    Article  Google Scholar 

  46. Friesen CK, Moore C, Kingstone A (2005) Does gaze direction really trigger a reflexive shift of spatial attention? Brain Cogn 57(1):66–69

    Article  Google Scholar 

  47. Goodwin MH, Goodwin C (1986) Gesture and coparticipation in the activity of searching for a word. Semiotica 62(1–2):51–76

    Article  Google Scholar 

  48. Graf HP, Cosatto E, Strom V, Huang FJ (2002) Visual prosody: Facial movements accompanying speech. In: International conference on automatic face and gesture recognition, pp 396–401

    Google Scholar 

  49. Green RF, Goldfried MR (1965) On the bipolarity of semantic space. Psychol Monogr Gen Appl 79(6, Whole No. 599):31

    Google Scholar 

  50. Gross R, Matthews I, Baker S (2005) Generic vs. person specific active appearance models. Image Vis Comput 23(12):1080–1093

    Article  Google Scholar 

  51. Gunes H, Pantic M (2010) Dimensional emotion prediction from spontaneous head gestures for interaction with sensitive artificial listeners. In: International conference on intelligent virtual agents, pp 371–377

    Chapter  Google Scholar 

  52. Haasch A, Hohenner S, Hüwel S, Kleinehagenbrock M, Lang S, Toptsis I, Fink GA, Fritsch J, Wrede B, Sagerer G (2004) Biron—the Bielefeld robot companion. In: Prassler E, Lawitzky G, Fiorini P, Haegele M (eds) International workshop on advances in service robotics, pp 27–32

    Google Scholar 

  53. Hadar U, Steiner TJ, Grant EC, Rose FC (1983) Kinematics of head movements accompanying speech during conversation. Hum Mov Sci 2(1–2):35–46

    Article  Google Scholar 

  54. Heylen D (2005) Challenges ahead: Head movements and other social acts in conversations. In: Joint symposium on virtual social agents, pp 45–52

    Google Scholar 

  55. Heylen D (2006) Head gestures, gaze and the principles of conversational structure. Int J Humanoid Robot 3(3):1–27

    Article  Google Scholar 

  56. Hugot V (2007) Eye gaze analysis in human-human interactions. Master’s thesis, KTH Royal Institute of Technology, School of Computer Science and Communication, Stockholm, Sweden

  57. Huppert FA, Whittington JE (2003) Evidence for the independence of positive and negative well-being: implications for quality of life assessment. Br J Health Psychol 8:107–122

    Article  Google Scholar 

  58. Ishikawa T, Baker S, Matthews I, Kanade T (2004) Passive driver gaze tracking with active appearance models. In: 11th World congress on intelligent transportation systems

    Google Scholar 

  59. Ivan P (2007) Active appearance models for gaze estimation. Master’s thesis, Vrije Universiteit Amsterdam, Faculty of Sciences, Business Mathematics & Informatics

  60. Izard CE (1971) The face of emotion. Appleton-Century-Crofts, New York

    Google Scholar 

  61. Izard CE (1994) Innate and universal facial expressions: Evidence from developmental and cross-cultural research. Psychol Bull 115(2):288–299

    Article  Google Scholar 

  62. Izard CE (2010) The many meanings/aspects of emotion: definitions, functions, activation, and regulation. Emot Rev 2(4):363–370

    Article  Google Scholar 

  63. Jakobs E, Manstead ASR, Fischer AH (2001) Social context effects on facial activity in a negative emotional setting. Emotion 1(1):51–69

    Article  Google Scholar 

  64. Kapoor A, Picard RW (2005) Multimodal affect recognition in learning environments. In: ACM international conference on multimedia. ACM Press, New York, pp 677–682

    Google Scholar 

  65. Kleck RE, Vaughan RC, Cartwright-Smith J, Vaughan KB, Colby CZ, Lanzetta JT (1976) Effects of being observed on expressive, subjective, and physiological responses to painful stimuli. J Pers Soc Psychol 34(6):1211–1218

    Article  Google Scholar 

  66. Kobayashi H, Kohshima S (1997) Unique morphology of the human eye. Nature 387:767–768

    Article  Google Scholar 

  67. Kraut RE (1982) Social presence, facial feedback, and emotion. J Pers Soc Psychol 42(5):853–863

    Article  Google Scholar 

  68. Kraut RE, Johnston RE (1979) Social and emotional messages of smiling: An ethological approach. J Pers Soc Psychol 37(9):1539–1553

    Article  Google Scholar 

  69. Lang C, Hanheide M, Lohse M, Wersing H, Sagerer G (2009) Feedback interpretation based on facial expressions in human-robot interaction. In: International symposium on robot and human interactive communication (RO-MAN), pp 189–194

    Google Scholar 

  70. Lang C, Wachsmuth S, Hanheide M, Wersing H (2012) Facial communicative signal interpretation in human-robot interaction by discriminative video subsequence selection. Tech. rep., Bielefeld University, Faculty of Technology, Research Institute for Cognition and Robotics/Applied Informatics

  71. Lang C, Wachsmuth S, Wersing H, Hanheide M (2010) Facial expressions as feedback cue in human-robot interaction—a comparison between human and automatic recognition performances. In: Workshop on CVPR for human communicative behavior analysis (CVPR4HB), pp 79–85

    Google Scholar 

  72. Langton SRH, Watt RJ, Bruce V (2000) Do the eyes have it? Cues to the direction of social attention. Trends Cogn Sci 4(2):50–59

    Article  Google Scholar 

  73. Lanitis A, Taylor CJ, Cootes TF (1997) Automatic interpretation and coding of face images using flexible models. Pattern Anal Mach Intell 19(7):743–756

    Article  Google Scholar 

  74. Lee J, Chao C, Bobick AF, Thomaz AL (2012) Multi-cue contingency detection. Int J Soc Robot. doi:10.1007/s12369-011-0136-5

    Google Scholar 

  75. Lohan KS, Rohlfing KJ, Pitsch K, Saunders J, Lehmann H, Nehaniv CL, Fischer K, Wrede B (2012) Tutor spotter: Proposing a feature set and evaluating it in a robotic system. Int J Soc Robot. doi:10.1007/s12369-011-0125-8

    Google Scholar 

  76. Lohan KS, Vollmer AL, Fritsch J, Rohlfing K, Wrede B (2009) Which ostensive stimuli can be used for a robot to detect and maintain tutoring situations. In: International workshop on social signal processing

    Google Scholar 

  77. Lohse M, Hanheide M (2008) Evaluating a social home tour robot applying heuristics. In: Workshop robots as social actors at RO-MAN

    Google Scholar 

  78. Lohse M, Rohlfing KJ, Wrede B, Sagerer G (2008) Try something else! when users change their discursive behavior in human-robot interaction. In: International conference on robotics and automation

    Google Scholar 

  79. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  80. Lucey S, Ashraf AB, Cohn JF (2007) Face recognition. In: Investigating spontaneous facial action recognition through AAM representations of the face. Itech Education and Publishing, Vienna, pp 275–286

    Google Scholar 

  81. Ma Y, Konishi Y, Kinoshita K, Lao S, Kawade M (2006) Sparse Bayesian regression for head pose estimation. In: International conference on pattern recognition, pp 507–510

    Google Scholar 

  82. Matthews I, Baker S (2004) Active appearance models revisited. Int J Comput Vis 60:135–164

    Article  Google Scholar 

  83. Maynard-Smith J, Harper D (2004) Animal signals. Oxford University Press, London

    Google Scholar 

  84. McAndrew FT (1986) A cross-cultural study of recognition thresholds for facial expressions of emotion. J Cross-Cult Psychol 17:211–224

    Article  Google Scholar 

  85. McLachlan JFC (1976) A short adjective check list for the evaluation of anxiety and depression. J Clin Psychol 32(1):195–197

    Article  MathSciNet  Google Scholar 

  86. McNaira DM, Lorr M (1964) An analysis of mood in neurotics. J Abnorm Soc Psychol 69(6):620–627

    Article  Google Scholar 

  87. Mehrabian A, Friar JT (1969) Encoding of attitude by a seated communicator via posture and position cues. J Consult Clin Psychol 33(3):330–336

    Article  Google Scholar 

  88. Mehrabian A, Russell JA (1974) An approach to environmental psychology. The MIT Press, Cambridge

    Google Scholar 

  89. Morimoto CH, Mimica MR (2005) Eye gaze tracking techniques for interactive applications. Comput Vis Image Underst 98(1):4–24

    Article  Google Scholar 

  90. Murphy-Chutorian E, Trivedi MM (2009) Head pose estimation in computer vision: a survey. Pattern Anal Mach Intell 31(4):607–626

    Article  Google Scholar 

  91. Newman R, Matsumoto Y, Rougeaux S, Zelinsky A (2000) Real-time stereo tracking for head pose and gaze estimation. In: International conference on automatic face and gesture recognition, pp 122–128

    Chapter  Google Scholar 

  92. Niit T, Valsiner J (1977) Recognition of facial expressions: An experimental investigation of Ekman’s model. Ada Comment Univ Tarvensis 429:85–107

    Google Scholar 

  93. Nowozin S, Bakir G, Tsuda K (2007) Discriminative subsequence mining for action classification. In: International conference on computer vision, pp 1–8

    Google Scholar 

  94. Osgood C, Suci G, Tannenbaum P (1957) The measurement of meaning. University of Illinois Press, Urbana

    Google Scholar 

  95. Osgood CE (1966) Dimensionality of the semantic space for communication via facial expressions. Scand J Psychol 7(1):1–30

    Article  Google Scholar 

  96. Pantic M, Rothkrantz LJM (2000) Automatic analysis of facial expressions: the state of the art. Pattern Anal Mach Intell 22(12):1424–1445

    Article  Google Scholar 

  97. Parkinson B (2005) Do facial movements express emotions or communicate motives? Personal Soc Psychol Rev 9(4):278–311

    Article  Google Scholar 

  98. Poggi I, D’Errico F, Vincze L (2010) Types of nods. the polysemy of a social signal. In: International conference on language resources and evaluation, pp 17–23

    Google Scholar 

  99. Posner J, Russell JA, Peterson BS (2005) The circumplex model of affect: an integrative approach to affective neuroscience cognitive development, and psychopathology. Dev Psychopathol 17:715–734

    Article  Google Scholar 

  100. Prado JA, Simplício C, Lori NF, Dias J (2012) Visuo-auditory multimodal emotional structure to improve human-robot-interaction. Int J Soc Robot 4(1):29–51

    Article  Google Scholar 

  101. Provine RR, Fischer KR (1989) Laughing, smiling, and talking: relation to sleeping and social context in humans. Ethology 83(4):295–305

    Article  Google Scholar 

  102. Rabie A, Lang C, Hanheide M, Castrillón-Santana M, Sagerer G (2008) Automatic initialization for facial analysis in interactive robotics. In: International conference on computer vision systems, pp 517–526

    Chapter  Google Scholar 

  103. Rabie A, Wrede B, Vogt T, Hanheide M (2009) Evaluation and discussion of multi-modal emotion recognition. In: International conference on computer and electrical engineering, vol 1, pp 598–602

    Chapter  Google Scholar 

  104. Ricciardelli P, Baylis G, Driver J (2000) The positive and negative of human expertise in gaze perception. Cognition 77(1):B1–B14

    Article  Google Scholar 

  105. Russell JA (1978) Evidence of convergent validity on the dimensions of affect. J Cross-Cult Psychol 36(10):1152–1168

    Google Scholar 

  106. Russell JA (1979) Affective space is bipolar. J Pers Soc Psychol 37(3):345–356

    Article  Google Scholar 

  107. Russell JA (1980) A circumplex model of affect. J Pers Soc Psychol 39(6):1161–1178

    Article  Google Scholar 

  108. Russell JA (1994) Is there universal recognition of emotion from facial expression? a review of the cross-cultural studies. Psychol Bull 115(1):102–141

    Article  Google Scholar 

  109. Russell JA (1995) Facial expressions of emotion: what lies beyond minimal universality? Psychol Bull 118(3):379–391

    Article  Google Scholar 

  110. Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. Acoust Speech Signal Process 26(1):43–49

    Article  MATH  Google Scholar 

  111. Sander D, Grandjean D, Kaiser S, Wehrle T, Scherer KR (2007) Interaction effects of perceived gaze direction and dynamic facial expression: evidence for appraisal theories of emotion. Eur J Cogn Psychol 19(3):470–480

    Article  Google Scholar 

  112. Sebe N, Cohen I, Gevers T, Huang TS (2006) Emotion recognition based on joint visual and audio cues. In: International conference on pattern recognition, vol 1, pp 1136–1139

    Google Scholar 

  113. Sebe N, Lew MS, Sun Y, Cohen I, Gevers T, Huang TS (2007) Authentic facial expression analysis. Image Vis Comput 25(12):1856–1863

    Article  Google Scholar 

  114. Shacham S, Dar R, Cleeland CS (1984) The relationship of mood state to the severity of clinical pain. Pain 18(2):187–197

    Article  Google Scholar 

  115. Shimada M, Yoshikawa Y, Asada M, Saiwaki N, Ishiguro H (2011) Effects of observing eye contact between a robot and another person. Int J Soc Robot 3(2):143–154

    Article  Google Scholar 

  116. Snider JG, Osgood CE (eds) (1969) Semantic differential technique. Aldine, Chicago

    Google Scholar 

  117. Storti C (2007) Speaking of India: bridging the communication gap when working with Indians. In: Yes, no, and other problems. Nicholas Brealey, Boston. pp 35–76

    Google Scholar 

  118. Thayer RE (1967) Measurement of activation through self-report. Psychol Rep 20(2):663–678

    Article  Google Scholar 

  119. Tian YL, Kanade T, Cohn JF (2001) Recognizing action units for facial expression analysis. Pattern Anal Mach Intell 23(2):97–115

    Article  Google Scholar 

  120. Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244

    MathSciNet  MATH  Google Scholar 

  121. Valstar MF, Gunes H, Pantic M (2007) How to distinguish posed from spontaneous smiles using geometric features. In: International conference on multimodal interfaces, pp 38–45

    Chapter  Google Scholar 

  122. Varchmin AC, Rae R, Ritter H (1997) Image based recognition of gaze direction using adaptive methods. In: Gesture and sign language in human-computer interaction. International gesture workshop, Bielefeld, Germany

    Google Scholar 

  123. von Grünau M, Anston C (1995) The detection of gaze direction: a stare-in-the-crowd effect. Perception 24(11):1297–1313

    Article  Google Scholar 

  124. Wang JG, Sung E (2002) Study on eye gaze estimation. Syst Man Cybern 32(3):332–350

    Article  Google Scholar 

  125. Wang JG, Sung E (2007) Em enhancement of 3d head pose estimated by point at infinity. Image Vis Comput 25:1864–1874

    Article  Google Scholar 

  126. Watson OM (1970) Proxemic behavior: a cross-cultural study. Mouton De Gruyter, Berlin

    Google Scholar 

  127. Westbrook MT (1976) Positive affect: A method of content analysis for verbal samples. J Consult Clin Psychol 44(5):715–719

    Article  Google Scholar 

  128. Widen SC, Russell JA (2010) Descriptive and prescriptive definitions of emotion. Emot Rev 2(4):377–378

    Article  Google Scholar 

  129. Yang P, Liu Q, Cui X, Metaxas DN (2008) Facial expression recognition based on dynamic binary patterns. In: Conference on computer vision and pattern recognition

    Google Scholar 

  130. Yeasin M, Bullot B, Sharma R (2006) Recognition of facial expressions and measurement of levels of interest from video. Multimedia 8(3):500–508

    Article  Google Scholar 

  131. Yoo DH, Kim JH, Lee BR, Chung MJ (2002) Non-contact eye gaze tracking system by mapping of corneal reflections. In: International conference on automatic face and gesture recognition, pp 94–99

    Google Scholar 

  132. Zeng Z, Pantic M, Roisman GI, Huang TS (2009) A survey of affect recognition methods: audio, visual, and spontaneous expressions. Pattern Anal Mach Intell 31(1):39–58

    Article  Google Scholar 

  133. Zhao G, Chen L, Song J, Chen G (2007) Large head movement tracking using sift-based registration. In: International conference on multimedia, pp 807–810

    Google Scholar 

Download references

Acknowledgements

Christian Lang gratefully acknowledges the financial support from Honda Research Institute Europe for the project “Facial Expressions in Communication”. The authors thank the anonymous reviewers for their helpful comments on an earlier draft of this paper.

Author information

Authors and Affiliations

  1. Research Institute for Cognition and Robotics (CoR-Lab), Bielefeld University, Bielefeld, Germany

    Christian Lang

  2. Applied Informatics, Bielefeld University, Bielefeld, Germany

    Sven Wachsmuth

  3. School of Computer Science, University of Lincoln, Lincoln, UK

    Marc Hanheide

  4. Honda Research Institute Europe, Offenbach, Germany

    Heiko Wersing

Authors
  1. Christian Lang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sven Wachsmuth
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marc Hanheide
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Heiko Wersing
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christian Lang.

Additional information

This work has been supported by the Honda Research Institute Europe, Offenbach, Germany.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lang, C., Wachsmuth, S., Hanheide, M. et al. Facial Communicative Signals. Int J of Soc Robotics 4, 249–262 (2012). https://doi.org/10.1007/s12369-012-0145-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12369-012-0145-z

Keywords

Search

Navigation