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Multiple-Classifiers Based Hand Gesture Recognition

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Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 662))

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Abstract

Gesture recognition technology is important in the field of human-computer interaction (HCI), the gesture recognition technology which is based on visual is sensitive to the impact of the environment. We proposed a multiple-classifiers based gesture recognition algorithm that recognizes ten kinds of gesture. The algorithm gets the size and the direction of the gesture by hand tracking algorithm which has the ability of segmentation and can give us the rough outline of the tracked hand. Based on this information, we rotate the image and get the upright image of the hand gesture. Then we extract the HOG feature of the upright hand image, and use multiple classifiers to classify the gesture. The algorithm has a better recognition rate whether the background color is similar to the skin or complex.

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References

  1. Panwar, M.: Hand gesture recognition based on shape parameters. In: 2012 International Conference on Computing, Communication and Applications (ICCCA), pp. 1–6. IEEE (2012)

    Google Scholar 

  2. Ahuja, M.K., Singh, A.: Static vision based Hand Gesture recognition using principal component analysis. In: IEEE International Conference on Moocs, Innovation and Technology in Education. IEEE (2015)

    Google Scholar 

  3. Dardas, N.H., Georganas, N.D.: Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Trans. Instrum. Meas. 60(11), 3592–3607 (2011)

    Article  Google Scholar 

  4. Joshi, G., Vig, R.: Histograms of orientation gradient investigation for static hand gestures. In: 2015 International Conference on Computing, Communication & Automation (ICCCA), pp. 1100–1103. IEEE (2015)

    Google Scholar 

  5. Guo, W., Wang, X.: Hand gesture detection technology based on HOG and SVM. Electron. Sci. Technol. 27(8), 15–18 (2014)

    Google Scholar 

  6. Wang, H., Sang, N., Yan, Y.: Real-time tracking combined with object segmentation. In: International Conference on Pattern Recognition, pp. 4098–4103. IEEE Computer Society (2014)

    Google Scholar 

  7. Rother, C., Kolmogorov, V., Blake, A.: Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. (TOG) 23(3), 309–314 (2004). ACM

    Article  Google Scholar 

  8. Fitzgibbon, A., Pilu, M., Fisher, R.B.: Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 476–480 (1999)

    Article  Google Scholar 

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Authors and Affiliations

  1. Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology), Ministry of Education, School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Simin Li, Zihan Ni & Nong Sang

Authors
  1. Simin Li
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  2. Zihan Ni
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  3. Nong Sang
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Corresponding author

Correspondence to Nong Sang .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Li, S., Ni, Z., Sang, N. (2016). Multiple-Classifiers Based Hand Gesture Recognition. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_13

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  • DOI: https://doi.org/10.1007/978-981-10-3002-4_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3001-7

  • Online ISBN: 978-981-10-3002-4

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