A Comparative Study of Different Image Features for Hand Gesture Machine Learning

Paulo Trigueiros, Fernando Ribeiro, Luis Paulo Reis

2013

Abstract

Vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition. Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. In this paper we present a comparative study of seven different algorithms for hand feature extraction, for static hand gesture classification, analysed with RapidMiner in order to find the best learner. We defined our own gesture vocabulary, with 10 gestures, and we have recorded videos from 20 persons performing the gestures for later processing. Our goal in the present study is to learn features that, isolated, respond better in various situations in human-computer interaction. Results show that the radial signature and the centroid distance are the features that when used separately obtain better results, being at the same time simple in terms of computational complexity.

References

  1. Alpaydin, E. (2004). Introduction to Machine Learning, MIT Press.
  2. Barczak, A. L. C., A. Gilman, et al. (2011). Analysis of Feature Invariance and Discrimination for Hand Images: Fourier Descriptors versus Moment Invariants. International Conference Image and Vision Computing. New Zeland.
  3. Ben-Hur, A. and J. Weston (2008). A User's Guide to Support Vector Machines. Data Mining Techniques for the Life Sciences, Humana Press. 609: 223-239.
  4. Blum, A. and T. Mitchell (1998). Combining labeled and unlabeled data with co-training. Proceedings of the eleventh annual conference on Computational learning theory. Madison, Wisconsin, United States, ACM: 92- 100.
  5. Bourennane, S. and C. Fossati (2010). "Comparison of shape descriptors for hand posture recognition in video." Signal, Image and Video Processing 6(1): 147- 157.
  6. Camastra, F. and A. Vinciarelli (2008). Machine Learning for Audio, Image and Video Analysis, Springer.
  7. Chang, C.-C. and C.-J. Lin (2011). "LIBSVM: A library for support vector machines." ACM Transactions on Intelligent Systems and Technology 2(3): 27.
  8. Conseil, S., S. Bourenname, et al. (2007). Comparison of Fourier Descriptors and Hu Moments for Hand Posture Recognition. 15th European Signal Processing Conference (EUSIPCO). Poznan, Poland: 1960-1964.
  9. Dalal, N. and B. Triggs (2005). Histograms of Oriented Gradients for Human Detection. International Conference on Computer Vision & Pattern Recognition, Grenoble, France.
  10. Dalal, N., B. Triggs, et al. (2006). Human detection using oriented histograms of flow and appearance. 9th European conference on Computer Vision. Graz, Austria, Springer-Verlag 428-441.
  11. Ding, Y., H. Pang, et al. (2011). "Static Hand-Gesture Recognition using HOG and Improved LBP features." International Journal of Digital Content Technology and its Applications 5(11): 236-243.
  12. Fang, Y., J. Cheng, et al. (2008). Hand posture recognition with co-training. 19th International Conference on Pattern Recognition (ICPR 2008). , Tampa, FL.
  13. Faria, B. M., N. Lau, et al. (2009). Classification of Facial Expressions Using Data Mining and machine Learning Algorithms. 4ª Conferência Ibérica de Sistemas e Tecnologias de Informação, Póvoa de Varim, Portugal.
  14. Faria, B. M., L. P. Reis, et al. (2010). Machine Learning Algorithms applied to the Classification of Robotic Soccer Formations ans Opponent Teams. IEEE Conference on Cybernetics and Intelligent Systems (CIS). Singapore: 344 - 349
  15. Freeman, W. T. and M. Roth (1994). Orientation Histograms for Hand Gesture Recognition, Mitsubishi Electric Research Laboratories, Cambridge Research Center.
  16. Gillian, N. E. (2011). Gesture Recognition for Musician Computer Interaction. Doctor of Philosophy, Faculty of Arts, Humanities and Social Sciences.
  17. Harris, C. and M. Stephens (1988). A combined corner and edge detector. The Fourth Alvey Vision Conference.
  18. Hninn, T. and H. Maung (2009). "Real-Time Hand Tracking and Gesture Recognition System Using Neural Networks." 50(Frebuary): 466-470.
  19. Hruz, M., J. Trojanova, et al. (2011). "Local binary pattern based features for sign language recognition." Pattern Recognition and Image Analysis 21(3): 398-401.
  20. Huynh, D. Q. (2009). Evaluation of Three Local Descriptors on Low Resolution Images for Robot Navigation. Image and Vision Computing (IVCNZ 7809). Wellington: 113 - 118
  21. Kaaniche, M.-B. and F. Bremond (2009). Tracking HOG Descriptors for Gesture Recognition. IEEE Int. Conf. on Advanced Video and Signal based Surveillance, IEEE Computer Society Press.
  22. Ke, W., W. Li, et al. (2010). "Real-Time Hand Gesture Recognition for Service Robot." 976-979.
  23. Lockton, R. (2002). Hand Gesture Recognition Using Computer Vision, Oxford University.
  24. Lowe, D. G. (2004). "Distinctive image features from scale-invariant keypoints." International Journal of Computer Vision 60(2): 91-110.
  25. Lu, W.-L. and J. J. Little (2006). Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor. Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision, IEEE Computer Society: 6.
  26. Maldonado-Báscon, S., S. Lafuente-Arroyo, et al. (2007). Road-Sign detection and Recognition Based on Support Vector Machines. IEEE Transactions on Intelligent Transportation Systems. 8: 264-278.
  27. Mannini, A. and A. M. Sabatini (2010). "Machine learning methods for classifying human physical activity from on-body accelerometers." Sensors 10(2): 1154-1175.
  28. Mitra, S. and T. Acharya (2007). Gesture recognition: A Survey. IEEE Transactions on Systems, Man and Cybernetics, IEEE. 37: 311-324.
  29. Murthy, G. R. S. and R. S. Jadon (2009). "A Review of Vision Based Hand Gestures Recognition." International Journal of Information Technology and Knowledge Management 2(2): 405-410.
  30. Ojala, T., M. PeitiKainen, et al. (2002). "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns." IEEE Trans. Pattern Analysis ans Machine Intelligence 24(7): 971-987.
  31. Ong, S. and S.Ranganath (2005). "Automatic sign language analysis: A survey and the future beyond lexical meaning." IEEE Trans. Pattern Analysis ans Machine Intelligence 27(6): 873-891.
  32. PietiKainen, M., A. Hadid, et al. (2011). Computer Vision Using Local Binary Patterns. London, SpringerVerlag.
  33. Pietikainen, M., T. Ojala, et al. (2000). "Rotation-Invariant Texture Classification using Feature Distributions." Pattern Recognition 33: 43-52.
  34. Roth, M., K. Tanaka, et al. (1998). Computer Vision for Interactive Computer Graphics. IEEE Computer Graphics And Applications, Mitsubishi Electric Research Laboratory: 42-53.
  35. Shi, J. and C. Tomasi (1994). Good Features to Track. Internacional Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA, Springer: 593-600.
  36. Shih, F. Y. (2008). Image Processing and Pattern Recognition: Fundamentals and Techniques. Canada, Wiley and Sons.
  37. Snyder, W. E. and H. Qi (2004). Machine Vision, Cambridge University Press.
  38. Stephan, J. J. and S. Khudayer (2010). "Gesture Recognition for Human-Computer Interaction (HCI)." International Journal of Advancements in Computing Technology 2(4): 30-35.
  39. Tara, R. Y., P. I. Santosa, et al. (2012). "Sign Language Recognition in Robot Teleoperation using Centroid Distance Fourier Descriptors." International Journal of Computer Applications 48(2).
  40. Treiber, M. (2010). An Introduction to Object Recognition, Springer.
  41. Trigueiros, P., F. Ribeiro, et al. (2012). A comparison of machine learning algorithms applied to hand gesture recognition. 7ª Conferência Ibérica de Sistemas e Tecnologias de Informação, Madrid, Spain.
  42. Unay, D., A. Ekin, et al. (2007). Robustness of Local Binary Patterns in Brain MR Image Analysis. 29th Annual Conference of the IEEE EMBS, Lyon, France, IEEE.
  43. Vicen-Bueno, R., R. Gil-Pita, et al. (2004). Complexity Reduction in Neural Networks Appplied to Traffic Sign Recognition Tasks.
  44. Wang, C.-C. and K.-C. Wang (2008). Hand Posture Recognition Using Adaboost with SIFT for Human Robot Interaction. Proceedings of the International Conference on Advanced Robotics (ICAR'07), Jeju, Korea.
  45. Witten, I. H., E. Frank, et al. (2011). Data Mining - Pratical Machine Learning Tools and Techniques, Elsevier.
  46. Zhang, D. and G. Lu (2002). A comparative Study of Fourier Descriptors for Shape Representation and Retrieval. Proc. of 5th Asian Conference on Computer Vision (ACCV), Melbourne, Australia, Springer.
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Paper Citation


in Harvard Style

Trigueiros P., Ribeiro F. and Reis L. (2013). A Comparative Study of Different Image Features for Hand Gesture Machine Learning . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, pages 51-61. DOI: 10.5220/0004200100510061


in Bibtex Style

@conference{icaart13,
author={Paulo Trigueiros and Fernando Ribeiro and Luis Paulo Reis},
title={A Comparative Study of Different Image Features for Hand Gesture Machine Learning},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2013},
pages={51-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004200100510061},
isbn={978-989-8565-39-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Comparative Study of Different Image Features for Hand Gesture Machine Learning
SN - 978-989-8565-39-6
AU - Trigueiros P.
AU - Ribeiro F.
AU - Reis L.
PY - 2013
SP - 51
EP - 61
DO - 10.5220/0004200100510061