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Link to original content: https://link.springer.com/doi/10.1007/s12652-020-02314-2
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RETRACTED ARTICLE: Development of hand gesture recognition system using machine learning

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This article was retracted on 27 June 2022

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Abstract

Human computer interaction (HCI) systems are increasing due to the demand for non-intrusive methods for communicating with machines. In this research article, vision based hand gesture recognition (HGR) System has been proposed using machine learning. This proposed system consists of three stages: segmentation, feature extraction and classification. The developed system is to be trained and tested using Sebastian Marcel static hand posture database which is available online. Discrete wavelet transform (DWT) along with modified Speed Up Robust Feature extraction technique has been used to extract rotation and scale invariant key descriptors. Then Bag of Word technique is used to develop the fixed dimension input vector that is required for the support vector machine. The classification accuracy of class 2 and class 4 which corresponds to the ‘No’ and ‘grasp’ gesture has reached 98%. The overall classification accuracy of the HGR system using SVM classifier is 96.5% with a recognition time of 0.024 s. Due to fast recognition time, this system can be employed in real time gesture image recognition system. Our HGR system addresses the complex background problem and also improves the robustness of hand gesture recognition.

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References

  • (1994) Chasing the colour glove: visual hand tracking. In: Chasing the colour glove: visual hand tracking

  • Bag-of-words model in computer vision. Wikipedia (Online). Available: https://en.wikipedia.org/wiki/Bag-of-words_model_in_computer_vision. Accessed May 2019

  • Bay H, Tuytelaars T, Van Gool L (2008) Speeded up robust features. In: Computer vision and image understanding, pp 346–359

  • Chen G, Bui TD (1999) Invariant fourier-wavelet descriptor for pattern recognition. Pattern Recogn 32:1083–1088

    Article  Google Scholar 

  • Chen Q, Georganas ND, Petriu EM (2008) Hand gesture recognition using Haar-like features and a stochastic context-free grammar. IEEE Trans Instrum Meas 57:1562–1571

    Article  Google Scholar 

  • Chen Q, Georganas ND, Petriu EM (2007) Real-time vision-based hand gesture recognition using haar-like features. In: IEEE instrumentation and measurement technology conference proceedings

  • Chuang GH, Kuo CC (1996) Wavelet descriptor of planar curves: theory and applications. IEEE Trans Image Process 5(1):56–70

    Article  Google Scholar 

  • Chung WK, Wu X, Xu Y (2009) A real-time hand gesture recognition based on Haar wavelet representation. In: IEEE int. conf. robot. biomimetics

  • Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Computer vision and pattern recognition

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

    Article  Google Scholar 

  • Dipak Kumar Ghosh SA (2011) A static hand gesture recognition algorithm using K-mean based RBFNN. In: ICICS

  • Elsayed RA, Sayed MS, Abdalla MI (2015) Skin-based adaptive background subtraction for hand gesture segmentation. In: CECS

  • Escalera S, Athitsos V, Guyon I (2016) Challenges in multimodal gesture recognition. J Mach Learn Res 17(72):1–54

    MathSciNet  Google Scholar 

  • Fu X, Lu J, Zhang T, Bonair C, Coats ML (2015) Wavelet enhanced image preprocessing and neural networks for hand gesture recognition. In: 2015 IEEE international conference on Smart City/SocialCom/SustainCom (SmartCity), Chengdu, China

  • Ghotkar AS, Kharate GK (2012) Hand Segmentation techniques to hand gesture recognition for natural human computer interaction. Int J Hum Comput Interact 3(1):15–25

    Google Scholar 

  • Haitham Hasan SK (2012) Static hand gesture recognition using neural networks. Springer Science+Business Media, Berlin

    Google Scholar 

  • He J, Zhang H (2008) A real-time face detection method in human-machine interaction. In: International conference on bioinformatics and biomedical engineering

  • Hsu CW, Chang CC, Lin CJ (2010) A practical guide to support vector classification. (Online). Available: https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.224.4115

  • Ke Y, Sukthankar R (2004) Pca-sift: a more distinctive representation for local image descriptors. In: IEEE conf. on computer vision and pattern recognition

  • LaViola J (1999) A survey of hand posture and gesture recognition techniques and technology. Department of Computer Science, Brown University, Providence

    Google Scholar 

  • Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE conference on computer vision and pattern recognition

  • Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110

    Article  Google Scholar 

  • Manresa C, Varona J, Mas R, Perales FJ (2000) Real-time hand tracking and gesture recognition for human computer interaction. Computer Vision Centre, University Autonomic, Barcelona

    Google Scholar 

  • Marcel S (1999) Hand posture recognition in a body-face centered space. In: Proc. conf. human factors comput. syst

  • Marcel S (2019) Sebastian Marcel hand posture and gesture datasets. (Online). Available: The hand gestures are obtained from https://www.idiap.ch/resource/gestures/website. Accessed Mar 2019

  • Morrison K, McKenna SJ (2004) An experimental comparison of trajectory-based and history-based representation for gesture recognition. In: Gesture-based communication in human–computer interaction, pp 152–163

  • Murthy G, Jadon R (2010) Hand gesture recognition using neural networks. In: 2010 IEEE 2nd international advance computing conference (IACC), Patiala, India

  • Piccialli F, Cuomo S, di Cola VS, Casolla G et al (2019) A machine learning approach for IoT cultural data. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01452-6

    Article  Google Scholar 

  • Power Glove (2018) (Online). Available: https://en.wikipedia.org/wiki/Power_Glove. Accessed 1 June 2018

  • Priyanka Parvathy D, Hema CR (2016) Hand Gesture Identification using Preprocessing. Background Subtraction and Segmentation Techniques. IJAER 11(5):3221–3228

    Google Scholar 

  • Priyanka Parvathy DP, Subramaniam K (2019) Rapid speedup segment analysis based feature extraction of hand gesture image, multimedia tools and applications. Springer US, New York, pp 1–16

    Google Scholar 

  • Rubine D (1991) Specifying gestures by example. In: SIGGRAPH

  • Shubhangi, Shinde G, Rajashri, Itkarkar R, Anilkumar V (2017) Gesture to speech conversion for sign language recognition. Int J Innov Adv Comput Sci 6(9)

  • Stiene S, Lingemann K, Nuchter A, Hertzberg J (2018) Contour-based object detection in range images. (Online). Available: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.73.640&rep=rep1&type=pdf. Accessed June 2018

  • Sturman DJ (1992) Whole-hand input. Massachusetts Institute, Cambridge

    Google Scholar 

  • Sturman DJ, Zeltzer D (1994) A survey of glove-based input. IEEE Comput Gr Appl 14(1):30–39

    Article  Google Scholar 

  • Sun LJ, Zhang LC (2010) Static sign language recognition based on edge gradient direction histogram. Microelectron Comput 27(3)

  • Varun M, Annadurai C (2020) PALM-CSS: a high accuracy and intelligent machine learning based cooperative spectrum sensing methodology in cognitive health care networks. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01859-6

    Article  Google Scholar 

  • Veltman SR, Prasad R (1994) Hidden Markov Models applied to on-linehandwritten isolated character recognition. EEE Trans Image Process 3:314–318

    Article  Google Scholar 

  • Wexelblat A (1995) An approach to natural gesture in virtual environments. ACM Trans Comput Hum Interact 2:179–200

    Article  Google Scholar 

  • Xu P (2017) A real-time hand gesture recognition and human-computer interaction system. (Online). Available: https://arxiv.org/pdf/1704.07296.pdf. Accessed June 2018

  • Yang J, Lu W, Waibel A (1998) Skin-color modeling and adaptation. In: ACCV

  • Yun L, Peng Z (2009) An automatic hand gesture recognition system based on Viola-Jones method and SVMs. In: Proc. 2nd int. workshop comput. sci. eng

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Correspondence to Priyanka Parvathy.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04226-9

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Parvathy, P., Subramaniam, K., Prasanna Venkatesan, G.K.D. et al. RETRACTED ARTICLE: Development of hand gesture recognition system using machine learning. J Ambient Intell Human Comput 12, 6793–6800 (2021). https://doi.org/10.1007/s12652-020-02314-2

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