Skip to main content
SpringerLink
Log in

Context-Aware Hand Pose Classifying Algorithm Based on Combination of Viola-Jones Method, Wavelet Transform, PCA and Neural Networks

  • Conference paper
  • First Online:
Context-Aware Systems and Applications (ICCASA 2016)

Included in the following conference series:

  • 359 Accesses

Abstract

In this paper we propose a novel context-aware algorithm for hand poses classifying. The proposed algorithm based on Viola-Jones method, wavelet transforms, PCA and neural networks. At first, the Viola-Jones method is used to find the location of hand pose in images. Then the features of hand pose are extracted using combination of wavelet transform and PCA. Finally, these extracted features are classified by multi-layer feedforward neural networks. In this proposed algorithm, for each training hand pose we create one neural network, which will determine whether an input hand pose is training hand pose or not. In order to test the proposed algorithm, we use known Cambridge Gesture database and divide it into 5 parts with difference light contrast conditions. The experimental results show that the proposed algorithm effectively classifies the hand pose in difference light contrast conditions and competes with state-of-the-art algorithms.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Viola, P., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, vol. 1. pp. 511–518 (2001)

    Google Scholar 

  2. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)

    Article  Google Scholar 

  3. Wang, Y.-Q.: An analysis of the Viola-Jones face detection algorithm. Image Process. On Line 4, 128–148 (2014)

    Article  Google Scholar 

  4. Phan, N.H., Bui, T.T.T., SpitsynVladimir, G.: Real-time hand gesture recognition base on Viola-Jones method, algorithm CAMShift, wavelet transform and principal component analysis. Tomsk State Univ. J. Control Comput. Sci. 2(23), 102–111 (2013)

    Google Scholar 

  5. Mehdi, L., Solimani, A., Dargazany, A.: Combining wavelet transforms and neural networks for image classification. In: 41st Southeasten Symposium on System Theory, Tullahoma, TN, USA, pp. 44–48 (2009)

    Google Scholar 

  6. Weibao, Z., Li, Y.: Image classification using wavelet coefficients in low-pass bands. In: Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, pp. 114–118 (2007)

    Google Scholar 

  7. Chang, T., Jay, K.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2(4), 429–440 (1993)

    Article  Google Scholar 

  8. Daniel, M.R.S., Shanmugam, A.: ANN and SVM based war scene classification using wavelet features: a comparative study. J. Comput. Inf. Syst. 7, 1402–1411 (2011)

    Google Scholar 

  9. Park, S.B., Lee, J.W., Kim, S.K.: Content-based image classification using a neural network. Pattern Recogn. Lett. 25, 287–300 (2004)

    Article  Google Scholar 

  10. Gonzalez, A.C., Sossa, J.H., Riveron, E.M.F., Pogrebnyak, O.: Histograms, wavelets and neural networks applied to image retrieval. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, pp. 820–827. Springer, Heidelberg (2006). doi:10.1007/11925231_78

    Chapter  Google Scholar 

  11. Lai, J.H., Yuen, P.C., Feng, G.C.: Face recognition using holistic Fourier invariant features. Pattern Recogn. 34, 95–109 (2001)

    Article  MATH  Google Scholar 

  12. Kakarwal, S., Dsehmuhk, R.: Wavelet transform based feature extraction for face recognition. Informatica 15(2), 243–250 (2004)

    Google Scholar 

  13. Zhang, B.-L., Zhang, H.: Face recognition by applying wavelet subband representation and kernel associative memory. IEEE Trans. Image Process. 4(11), 1549–1560 (1995)

    Article  Google Scholar 

  14. Gumus, E., Kilic, N., Sertbas, A., Ucan, O.N.: Evaluation of face recognition techniques using PCA, wavelets and SVM. Expert Syst. Appl. 37, 6404–6408 (2010)

    Article  Google Scholar 

  15. Wadkar, P.D., Wankhade, M.: Face recognition using discrete wavelet transform. Int. J. Adv. Eng. Technol. 3(1), 239–242 (2012)

    Google Scholar 

  16. Mazloom, M., Kasaei, K.: Face recognition using PCA, wavelets and neural networks. In: Proceeding of the First International Conference on Modeling, Simulation and Applied Optimization, Sharjah, UAE, 1–3 February, pp. 1–6 (2005)

    Google Scholar 

  17. Phan, N.H., Bui, T.T.T., Spitsyn, V.G., Bolotova, Y.A.: Using a Haar wavelet transform, principal component analysis and neural networks for OCR in the presence of impulse noise. J. Comput. Opt. 40(2), 249–257 (2016)

    Article  Google Scholar 

  18. Phan, N.H., Bui, T.T.T.: Context-aware handwritten and optical character recognition using a combination of wavelet transform, PCA and neural networks. In: Vinh, P.C., Alagar, V. (eds.) ICCASA 2015. LNICSSITE, vol. 165, pp. 254–263. Springer, Cham (2016). doi:10.1007/978-3-319-29236-6_25

    Chapter  Google Scholar 

  19. Phan, N.H., Bui, T.T.T., Spitsyn, V.G., Bolotova Yu, A., Savitsky Yu, V.: Development of algorithms for face and character recognition based on wavelet transforms, PCA and neural networks. In: Proceedings of 2015 International Siberian Conference on Control and Communications (SIBCON). IEEE (2015)

    Google Scholar 

  20. Phan, N.H., Bui, T.T.T., Spitsyn, V.G.: Face and hand gesture recognition based on wavelet transforms and principal component analysis. In: 7th International Forum on Strategic Technology IFOST: Proceedings of IFOST 2012. IEEE (2012)

    Google Scholar 

  21. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (2001)

    Google Scholar 

  22. Papageorgiou, C., Oren, M., Poggio, T.: A general framework for object detection. In: International Conference on Computer Vision (1998)

    Google Scholar 

  23. Kearns, M.: Thoughts on hypothesis boosting. Unpublished manuscript in Machine Learning Class Project (1988)

    Google Scholar 

  24. Freund, Y., Schapire, R.E.: A short introduction to boosting. J. Japan. Soc. Artif. Intell. 14(5), 771–780 (1999)

    Google Scholar 

  25. Kim, T.K., Wong, S.F., Cipolla, R.: Cambridge Hand Gesture Data set. http://www.iis.ee.ic.ac.uk/~tkkim/ges_db.htm

Download references

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Ba Ria-Vung Tau University, Truong Van Bang Street 01, Vung Tau City, Ba Ria-Vung Tau Province, Vietnam

    Ngoc Hoang Phan & Thi Thu Trang Bui

Authors
  1. Ngoc Hoang Phan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thi Thu Trang Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ngoc Hoang Phan .

Editor information

Editors and Affiliations

  1. Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam

    Phan Cong Vinh

  2. Thu Dau Mot University, Thu Dau Mot City, Vietnam

    Le Tuan Anh

  3. Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam

    Nguyen Thi Thuy Loan

  4. University of the Thai Chamber of Commerce, Bangkok, Thailand

    Waralak Vongdoiwang Siricharoen

Rights and permissions

Reprints and permissions

Copyright information

© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper

Phan, N.H., Bui, T.T.T. (2017). Context-Aware Hand Pose Classifying Algorithm Based on Combination of Viola-Jones Method, Wavelet Transform, PCA and Neural Networks. In: Cong Vinh, P., Tuan Anh, L., Loan, N., Vongdoiwang Siricharoen, W. (eds) Context-Aware Systems and Applications. ICCASA 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 193. Springer, Cham. https://doi.org/10.1007/978-3-319-56357-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-56357-2_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56356-5

  • Online ISBN: 978-3-319-56357-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation