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Decision Tree Based Single Person Gesture Recognition

  • Sriparna SahaEmail author
  • Shreyasi Datta
  • Amit Konar
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 837)

Abstract

This chapter is aimed at gesture recognition using the Kinect sensor. The Kinect sensor generates the human skeleton with twenty different 3-dimensional coordinates corresponding to twenty body joints. The present work requires eleven out of these twenty joints: six joints about the right and the left hands and five upper body joints. A unique set of twenty three features has been extracted to distinguish between gestures corresponding to five basic human emotional states, namely, ‘Anger’, ‘Fear’, ‘Happiness’, ‘Sadness’ and ‘Relaxation’. The features are based on the distances, accelerations, and angle between the different joints. The goal of the proposed system is to classify an emotion as positive or negative and determine its intensity level from the corresponding gestures. A high overall recognition rate of 86.8% is obtained from the proposed system using a decision tree based classifier.

References

  1. 1.
    Agrawal, Dev Drume, Shiv Ram Dubey, and Anand Singh Jalal. 2014. Emotion recognition from facial expressions based on multi-level classification. International Journal of Computational Vision and Robotics 4 (4): 365–389.CrossRefGoogle Scholar
  2. 2.
    Brown, Donald E., Vincent Corruble, and Clarence Louis Pittard. 1993. A comparison of decision tree classifiers with backpropagation neural networks for multimodal classification problems. Pattern Recognition 26 (6): 953–961.CrossRefGoogle Scholar
  3. 3.
    Clark, Ross A., Yong-Hao Pua, Karine Fortin, Callan Ritchie, Kate E. Webster, Linda Denehy, and Adam L. Bryant. 2012. Validity of the microsoft kinect for assessment of postural control. Gait & Posture 36 (3): 372–377.CrossRefGoogle Scholar
  4. 4.
    Das, Sauvik, Anisha Halder, Pavel Bhowmik, Aruna Chakraborty, Amit Konar, and A.K. Nagar. 2009. Voice and facial expression based classification of emotion using linear support vector machine. In 2009 Second International Conference on Developments in eSystems Engineering (DESE), 377–384. IEEE.Google Scholar
  5. 5.
    Dutta, Tilak. 2012. Evaluation of the kinect\(^\text{TM}\) sensor for 3-d kinematic measurement in the workplace. Applied Ergonomics 43 (4): 645–649.CrossRefGoogle Scholar
  6. 6.
    Friedl, Mark A. and Carla E. Brodley. 1997. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment 61 (3): 399–409.CrossRefGoogle Scholar
  7. 7.
    Halder, Anisha, Pratyusha Rakshit, Aruna Chakraborty, Amit Konar, and Ramadoss Janarthanan. 2011. Emotion recognition from the lip-contour of a subject using artificial bee colony optimization algorithm. In SEMCCO (1), vol. 7076, Lecture Notes in Computer Science, 610–617. Springer.Google Scholar
  8. 8.
    Kourosh Khoshelham and Sander Oude Elberink. 2012. Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors 12 (2): 1437–1454.CrossRefGoogle Scholar
  9. 9.
    Kohavi, Ron. 1996. Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid. In KDD, vol. 96, 202–207. Citeseer.Google Scholar
  10. 10.
    Konar, A., and A. Chakraborty. 2009. Emotional Intelligence: A Cybernetic Approach.Google Scholar
  11. 11.
    Konar, Amit. 2000. Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain. Boca Raton, FL, USA: CRC Press Inc.Google Scholar
  12. 12.
    Larsen, Randy J., and Edward Diener. 1992. Promises and Problems with the Circumplex Model of Emotion.Google Scholar
  13. 13.
    Le, Thi-Lan, Minh-Quoc Nguyen, et al. 2013. Human posture recognition using human skeleton provided by kinect. In 2013 International Conference on Computing, Management and Telecommunications (ComManTel), 340–345. IEEE.Google Scholar
  14. 14.
    Lee, Chi-Chun, Emily Mower, Carlos Busso, Sungbok Lee, and Shrikanth Narayanan. 2011. Emotion recognition using a hierarchical binary decision tree approach. Speech Communication 53 (9–10): 1162–1171.CrossRefGoogle Scholar
  15. 15.
    Liu, Yuanning, Zequn Zhang, Ao Li, and Minghui Wang. 2012. View independent human posture identification using kinect. In 2012 5th International Conference on Biomedical Engineering and Informatics (BMEI) 1590–1593. IEEE.Google Scholar
  16. 16.
    Monir, Samiul, Sabirat Rubya, and Hasan Shahid Ferdous. 2012. Rotation and scale invariant posture recognition using microsoft kinect skeletal tracking feature. In 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), 404–409. IEEE.Google Scholar
  17. 17.
    Nguyen, Thao, Iris Bass, Mingkun Li, and Ishwar K. Sethi. 2005. Investigation of combining svm and decision tree for emotion classification. In Seventh IEEE International Symposium on Multimedia 5 pp. IEEE.Google Scholar
  18. 18.
    Nihei, Yuma, Ippei Samejima, Naotaka Hatao, Hiroshi Takemura, and Satoshi Kagami. 2012. Map integration of human trajectory with sitting/standing position using lrf and kinect sensor. In 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), 1250–1255. IEEE.Google Scholar
  19. 19.
    Olaru, Cristina, and Louis Wehenkel. 2003. A complete fuzzy decision tree technique. Fuzzy sets and Systems 138 (2): 221–254.MathSciNetCrossRefGoogle Scholar
  20. 20.
    Olson, David H. 1986. Circumplex model vii: Validation studies and faces iii. Family Process 25 (3): 337–351.CrossRefGoogle Scholar
  21. 21.
    Olson, David H., Douglas H. Sprenkle, and Candyce S. Russell. 1979. Circumplex model of marital and family systems: I. cohesion and adaptability dimensions, family types, and clinical applications. Family Process 18 (1): 3–28.CrossRefGoogle Scholar
  22. 22.
    Olson, David H., Douglas H. Sprenkle, and Candyce S. Russell. 1983. Circumplex model of marital and family systems: VI. Theoretical update. Family Process 22 (1): 69–83.CrossRefGoogle Scholar
  23. 23.
    Parajuli, Monish, Dat Tran, Wanli Ma, and Dharmendra Sharma. 2012. Senior health monitoring using kinect. In Communications and Electronics (ICCE), 309–312. IEEE.Google Scholar
  24. 24.
    Plutchik, Robert and Hope R. 1997. Conte. Circumplex Models of Personality and Emotions. Washington, dc, UUS: American Psychological Association.Google Scholar
  25. 25.
    Posner, Jonathan, James A. Russell, and Bradley S. Peterson. 2005. The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and Psychopathology, 17 (3): 715–734.Google Scholar
  26. 26.
    Rounds, E.M. 1980. A combined nonparametric approach to feature selection and binary decision tree design. Pattern Recognition 12 (5): 313–317.CrossRefGoogle Scholar
  27. 27.
    Russell, James A. Maria Lewicka, and Toomas Niit. 1989. A cross-cultural study of a circumplex model of affect. Journal of Personality and Social Psychology, 57 (5): 848.CrossRefGoogle Scholar
  28. 28.
    Safavian, S. Rasou and David Landgrebe. 1991. A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics 21 (3): 660–674.MathSciNetCrossRefGoogle Scholar
  29. 29.
    Saha, Sriparna, Anupam Banerjee, Sumana Basu, Amit Konar, and Atulya K. Nagar. 2013. Fuzzy image matching for posture recognition in ballet dance. In FUZZ-IEEE 2013, Proceedings of IEEE International Conference on Fuzzy Systems, Hyderabad, India, 7–10 July, 2013.Google Scholar
  30. 30.
    Saha, Sriparna, Shreya Ghosh, Amit Konar, and Atulya K. Nagar. 2013. Gesture recognition from indian classical dance using kinect sensor. In Computational Intelligence, Communication Systems and Networks (CICSyN), 3–8. IEEE.Google Scholar
  31. 31.
    Schaefer, Earl S. 1959. A circumplex model for maternal behavior. The Journal of Abnormal and Social Psychology 59 (2): 226.CrossRefGoogle Scholar
  32. 32.
    Shlien, Seymour. 1990. Multiple binary decision tree classifiers. Pattern Recognition 23 (7): 757–763.CrossRefGoogle Scholar
  33. 33.
    Solaro, John. 2011. The kinect digital out-of-box experience. IEEE Computer 44 (6): 97–99.CrossRefGoogle Scholar
  34. 34.
    Tahir, Nooritawati Md, Aini Hussain, Salina Abdul Samad, and Hafizah Hussin. 2010. On the use of decision tree for posture recognition. In 2010 International Conference on Intelligent Systems, Modelling and Simulation (ISMS), 209–214. IEEE.Google Scholar
  35. 35.
    Tong, Jing, Jin Zhou, Ligang Liu, Zhigeng Pan, and Hao Yan. 2012. Scanning 3d full human bodies using kinects. IEEE Transactions on Visualization and Computer Graphics 18 (4): 643–650.CrossRefGoogle Scholar
  36. 36.
    Xia Junyi, and R. Alfredo Siochi. 2012. A real-time respiratory motion monitoring system using kinect: proof of concept. Medical Physics 39 (5): 2682–2685CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentMaulana Abul Kalam Azad University of TechnologyKolkataIndia
  2. 2.Electronics and Tele-communication Engineering DepartmentJadavpur UniversityKolkataIndia

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