Emotion Profiling: Ingredient for Rule based Emotion Recognition Engine

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Dr. Preeti Khanna
Dr. Preeti Khanna
2
Dr. Sasi M Kumar
Dr. Sasi M Kumar
1 SVKMs NMIMS - School of Business Management

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Emotions are considered to be the reflection of human thinking and decision-making process which increase his/her performance by producing an intelligent outcome. Hence it is a challenging task to embed the emotional intelligence in machine as well so that it could respond appropriately. However, present human computer interfaces still don’t fully utilize emotion feedback to create a more natural environment because the performance of the emotion recognition is still not very robust and reliable and far from real life experience. In this paper, we present an attempt in addressing this aspect and identifying the major challenges in the process. We introduce the concept of ’emotion profile’ to evaluate an individual feature as each feature irrespective of the modality has different capability for differentiating among the various subsets of emotions. To capture the discrimination across target emotions w.r.t. each feature we propose a framework for emotion recognition built around if-then rules using certainty factors to represent uncertainty and unreliability of individual features.

47 Cites in Articles

References

  1. Juan Martı́nez-Miranda,Arantza Aldea (2005). Emotions in human and artificial intelligence.
  2. J Foley (1996). JTEC Panel Report on Human Computer Interaction Technologies in Japan.
  3. C Lisetti,F Nasoz (2005). Affective intelligent car interfaces with emotion recognition HCI.
  4. G Bower (1981). Mood and Memory.
  5. R Zajonc (1984). On the primacy of affect..
  6. A Damasio (1994). Descartes' Error.
  7. Joseph Ledoux (1992). Brain mechanisms of emotion and emotional learning.
  8. Douglas Derryberry,Don Tucker (1992). Neural mechanisms of emotion..
  9. Jason Colquitt,Jeffrey Lepine,Raymond Noe (2000). Toward an integrative theory of training motivation: A meta-analytic path analysis of 20 years of research..
  10. N Frijda (1986). The Emotions.
  11. R Birdwhistle (1970). Kinesics and Context: Essays on Body Motion and Communication.
  12. P Ekman,W Friesen (1975). Unmasking the Face: A Guide to Recognizing Emotions from Facial Expressions.
  13. Nicole Chovil (1991). Discourse‐oriented facial displays in conversation.
  14. D Goleman (1995). Emotional Intelligence.
  15. L James,D Nahl (2000). Road Rage and Aggressive Driving: Steering Clear of highway Warfare.
  16. Preeti Khanna,M Sasikumar (2010). Recognizing emotions from human speech.
  17. P Khanna,S Kumar (2011). Application of Vector Quantization in Emotion Recognition from Human Speech.
  18. Preeti Khanna,M Sasikumar (2010). Recognising Emotions from Keyboard Stroke Pattern.
  19. R Picard (1997). Affective computing.
  20. De Silva,& Ng (2000). Bimodal Emotion Recognition, Automatic Face and Gesture Recognition.
  21. N Sebe,I Cohen,T Gevers,T Huang (2006). Emotion Recognition Based, On Joint Visual Emotion Profiling: Ingredient for Rule based Emotion Recognition Engine and Audio Cues.
  22. Z Zeng,Tu Jilin,Liu,Huang,Roth Pianfetti,Levinson (2007). Audio-Visual Affect Recognition.
  23. Jonghwa Kim,Elisabeth André (2006). Emotion Recognition Using Physiological and Speech Signal in Short-Term Observation.
  24. A Corradini,M Mehta,N Bernsen,J Martin (2003). Multimodal input fusion In Human computer interaction on the example of the on-going nice project.
  25. H Liao (2002). Ffowcs Williams, Prof. John Eirwyn, (born 25 May 1935), Rank Professor of Engineering, University of Cambridge, 1972–2002, now Emeritus; Master, Emmanuel College, Cambridge, 1996–2002 (Professorial Fellow, 1972–96; Life Fellow, 2002).
  26. Sanshzar Kettebekov,Rajeev Sharma (2000). UNDERSTANDING GESTURES IN MULTIMODAL HUMAN COMPUTER INTERACTION.
  27. R Sharma,V Pavlovic,T Huang (1998). Toward Multimodal Human Computer Interface.
  28. S Kumar,S Ramani,S Raman,K Anjaneyulu,R Chandrasekar (2007). Rule Based Expert Systems -A Practical Introduction.
  29. Edward Shortliffe,Bruce Buchanan (1975). A model of inexact reasoning in medicine.
  30. J Gordon,E Shortliffe (1984). The Dempster-Shafer Theory of Evidence.
  31. C Negoita (1985). Expert Systems and Fuzzy Systems.
  32. J Doyle (1979). Truth Maintenance System.
  33. R Reiter (1980). A logic for default reasoning.
  34. Drew Mcdermott,Jon Doyle (1980). Non-monotonic logic I.
  35. T Kanade,J Cohn,Yingli Tian (2000). Comprehensive database for facial expression analysis.
  36. A Azcarate,F Hageloh,K Van De Sande,R Valenti (2005). Automatic facial Emotion recognition.
  37. J Zhao,G Keearney (1996). Classifying Facial Emotions by Back propagation Neural Networks with Fuzzy Inputs.
  38. B Fasel,J Luettin (2003). Automatic Facial Analysis A Survey.
  39. M Pantie,L Rothkrantz (2000). Automatic analysis of facial expressions: the state of the art.
  40. N Sebe,M Lew,Y Sun,L Cohen,T Gevers,T Huang (2007). Authentic Facial Expression Analysis.
  41. M Pantic,L Rothkrantz (2000). Expert system for automatic analysis of facial expressions.
  42. H Kobayashi,F Hara (1992). Recognition of Six Basic Facial Expressions and Their Strength by Neural Network.
  43. G Edwards,T Cootes,C Taylor (1998). Face recognition using active appearance models.
  44. M Lyons,J Budynek,S Akamatsu (1999). Automatic classification of single facial images.
  45. Chung-Lin Huang,Yu-Ming Huang (1997). Facial Expression Recognition Using Model-Based Feature Extraction and Action Parameters Classification.
  46. H Hong,H Neven,C Von Malsburg,Der (1998). Online Facial Expression Recognition Based on Personalized Galleries.
  47. S Kulkarni,N Reddy,S Hariharan (2009). Facial expression (mood) recognition from facial images using committee neural networks.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Dr. Preeti Khanna. 2014. \u201cEmotion Profiling: Ingredient for Rule based Emotion Recognition Engine\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 14 (GJCST Volume 14 Issue F3): .

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GJCST Volume 14 Issue F3
Pg. 13- 23
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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v1.2

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August 21, 2014

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English

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Emotions are considered to be the reflection of human thinking and decision-making process which increase his/her performance by producing an intelligent outcome. Hence it is a challenging task to embed the emotional intelligence in machine as well so that it could respond appropriately. However, present human computer interfaces still don’t fully utilize emotion feedback to create a more natural environment because the performance of the emotion recognition is still not very robust and reliable and far from real life experience. In this paper, we present an attempt in addressing this aspect and identifying the major challenges in the process. We introduce the concept of ’emotion profile’ to evaluate an individual feature as each feature irrespective of the modality has different capability for differentiating among the various subsets of emotions. To capture the discrimination across target emotions w.r.t. each feature we propose a framework for emotion recognition built around if-then rules using certainty factors to represent uncertainty and unreliability of individual features.

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Emotion Profiling: Ingredient for Rule based Emotion Recognition Engine

Dr. Preeti Khanna
Dr. Preeti Khanna
Dr. Sasi M Kumar
Dr. Sasi M Kumar

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