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Hand gesture recognition based on concentric circular scan lines and weighted K-nearest neighbor algorithm

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Abstract

Human-computer interactions based on hand gestures are of the most popular natural interactive modes, which severely depends on real-time hand gesture recognition approaches. In this paper, a simple but effective hand feature extraction method is described, and the corresponding hand gesture recognition method is proposed. First, based on a simple tortoise model, we segment the human hand images by skin color features and tags on the wrist, and normalize them to create the training dataset. Second, feature vectors are computed by drawing concentric circular scan lines (CCSL) according to the center of the palm, and linear discriminant analysis (LDA) algorithm is used to deal with those vectors. Last, a weighted k-nearest neighbor (W-KNN) algorithm is presented to achieve real-time hand gesture classification and recognition. Besides the efficiency and effectiveness, we make sure that the whole gesture recognition system can be easily implemented and extended. Experimental results with a user-defined hand gesture dataset and multi-projector display system show the effectiveness and efficiency of the new approach.

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References

  1. Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185

    MathSciNet  Google Scholar 

  2. Chen TW, Chen YL, Chien SY (2008) Fast image segmentation based on k-means clustering with histograms in hsv color space. In: IEEE 10th workshop on multimedia signal processing, 2008, IEEE, pp 322–325

  3. Chen WL, Wu CH, Lin CH (2015) Depth-based hand gesture recognition using hand movements and defects. In: International symposium on next-generation electronics

  4. Darrell TJ, Pentland AP (1994) Classifying hand gestures with a view-based distributed representation. In: Advances in neural information processing systems, pp 945–952

  5. Dinh DL, Kim JT, Kim TS (2014) Hand gesture recognition and interface via a depth imaging sensor for smart home appliances. Energy Procedia 62:576–582

    Article  Google Scholar 

  6. Dinh DL, Lee S, Kim TS (2016) Hand number gesture recognition using recognized hand parts in depth images. Multimedia Tools and Applications 75(2):1333–1348

    Article  Google Scholar 

  7. Gregorio P (2008) Capacitive sensor gloves. US Patent App. 12/250,815

  8. Hasan H, Abdul-Kareem S (2014) Human–computer interaction using vision-based hand gesture recognition systems: a survey. Neural Comput Applic 25(2):251–261

    Article  Google Scholar 

  9. Huang R, Liu Q, Lu H, Ma S (2002) Solving the small sample size problem of lda. In: 16Th international conference on pattern recognition, 2002. Proceedings. IEEE, vol 3, pp 29–32

  10. Jadooki S, Mohamad D, Saba T, Almazyad AS, Rehman A (2016) Fused features mining for depth-based hand gesture recognition to classify blind human communication. Neural Comput Applic:1–10

  11. Kurakin A, Zhang Z, Liu Z (2012) A real time system for dynamic hand gesture recognition with a depth sensor, pp 1975–1979

  12. Kyperountas M, Tefas A, Pitas I (2007) Weighted piecewise lda for solving the small sample size problem in face verification. IEEE Trans Neural Netw 18(2):506–519

    Article  Google Scholar 

  13. Lee HK, Kim JH (1999) An hmm-based threshold model approach for gesture recognition. IEEE Trans Pattern Anal Mach Intell 21(10):961–973

    Article  Google Scholar 

  14. Lu Z, Lal Khan MS, Ur Réhman S (2013a) Hand and foot gesture interaction for handheld devices. In: Proceedings of the 21st ACM international conference on multimedia, ACM, pp 621–624

  15. Lu Z, et al. (2013b) Touch-less interaction smartphone on go!. In: SIGGRAPH Asia 2013 Posters, ACM, p 28

  16. Lv Z (2013) Wearable smartphone: wearable hybrid framework for hand and foot gesture interaction on smartphone. In: Proceedings of the IEEE international conference on computer vision workshops, pp 436–443

  17. Lv Z, Li H (2015) Imagining in-air interaction for hemiplegia sufferer. In: International conference on virtual rehabilitation proceedings (ICVR), 2015, IEEE, pp 149–150

  18. Lv Z, Réhman SU (2013) Multi-gesture based football game in smart phones

  19. Lv Z, Halawani A, Lal Khan MS, Réhman SU, Li H (2013) Finger in air: touch-less interaction on smartphone. In: Proceedings of the 12th international conference on mobile and ubiquitous multimedia, ACM, p 16

  20. Lv Z, Halawani A, Feng S, Li H, Réhman SU (2014) Multimodal hand and foot gesture interaction for handheld devices. ACM Trans Multimed Comput Commun Appl (TOMM) 11(1s):10

    Google Scholar 

  21. Lv Z, Halawani A, Feng S, Ur Réhman S, Li H (2015) Touch-less interactive augmented reality game on vision-based wearable device. Pers Ubiquit Comput 19(3-4):551–567

    Article  Google Scholar 

  22. Mehdi SA, Khan YN (2002) Sign language recognition using sensor gloves. In: Proceedings of the 9th international conference on neural information processing, 2002. ICONIP’02. IEEE, vol 5, pp 2204–2206

  23. Morency LP, Quattoni A, Darrell T (2007) Latent-dynamic discriminative models for continuous gesture recognition. In: IEEE Conference on computer vision and pattern recognition, 2007. CVPR’07. IEEE, pp 1–8

  24. Pu Q, Gupta S, Gollakota S, Patel S (2013) Whole-home gesture recognition using wireless signals. In: Proceedings of the 19th annual international conference on mobile computing & networking, ACM, pp 27–38

  25. Pu Q, Gupta S, Gollakota S, Patel S (2015) Gesture recognition using wireless signals. GetMobile: Mobile Computing and Communications 18(4):15–18

    Google Scholar 

  26. Reynolds DA, Quatieri TF, Dunn RB (2000) Speaker verification using adapted gaussian mixture models. Digital Signal Process 10(1):19–41

    Article  Google Scholar 

  27. Suarez J, Murphy RR (2012) Hand gesture recognition with depth images: a review. In: RO-MAN, 2012 IEEE, IEEE, pp 411–417

  28. Sural S, Qian G, Pramanik S (2002) Segmentation and histogram generation using the hsv color space for image retrieval. In: International conference on image processing. 2002. Proceedings. 2002, IEEE, vol 2, pp II–589

  29. Wang X (2007) Gesture recognition based on adaptive genetic algorithm. Journal of Computer-Aided design and Computer Craphics 19(8):1056

    Google Scholar 

  30. Wilson AD, Bobick AF (1999) Parametric hidden markov models for gesture recognition. IEEE Trans Pattern Anal Mach Intell 21(9):884–900

    Article  Google Scholar 

  31. Wu CH, Lin CH (2013) Depth-based hand gesture recognition for home appliance control , pp 279–280

  32. Wu CH, Chen WL, Lin CH (2015) Depth-based hand gesture recognition. Multimedia Tools and Applications:1–22

  33. Xie R, Cao J (2016) Accelerometer-based hand gesture recognition by neural network and similarity matching. IEEE Sensors J 16(11):4537–4545

    Article  Google Scholar 

  34. Xie Y, Ji Q (2002) A new efficient ellipse detection method. In: 16th international conference on pattern recognition, 2002. Proceedings. IEEE, vol 2, pp 957–960

  35. Yang B, Song X, Feng Z, Hao X (2010) Gesture recognition in complex background based on distribution features of hand. Journal of Computer-Aided design and Computer Craphics 22(10):1841–1848

    Google Scholar 

  36. Yao Y, Fu Y (2012) Real-time hand pose estimation from RGB- D sensor. In: IEEE international conference on multimedia and expo (ICME), 2012, IEEE, pp 705–710

  37. Yin L, Dong M, Duan Y, Deng W, Zhao K, Guo J (2014) A high-performance training-free approach for hand gesture recognition with accelerometer. Multimedia Tools and Applications 72(1):843–864

    Article  Google Scholar 

  38. Zhang QY, Hu JQ, Zhang MY (2010) Mean shift dynamic deforming hand gesture tracking algorithm based on region growth. Pattern Recognition and Artificial Intelligence:4

  39. Zivkovic Z (2004) Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004. IEEE, vol 2, pp 28–31

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Acknowledgments

This work is supported by National Science Foundation of China (Numbers: 61303146, 61602431).

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Correspondence to Ke Yan.

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Liu, Y., Wang, X. & Yan, K. Hand gesture recognition based on concentric circular scan lines and weighted K-nearest neighbor algorithm. Multimed Tools Appl 77, 209–223 (2018). https://doi.org/10.1007/s11042-016-4265-6

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  • DOI: https://doi.org/10.1007/s11042-016-4265-6

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