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RETRACTED ARTICLE: Static hand gesture recognition using neural networks

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This article was retracted on 16 February 2017

Abstract

This paper presents a novel technique for hand gesture recognition through human–computer interaction based on shape analysis. The main objective of this effort is to explore the utility of a neural network-based approach to the recognition of the hand gestures. A unique multi-layer perception of neural network is built for classification by using back-propagation learning algorithm. The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predefined finite number of gesture classes. The proposed system presents a recognition algorithm to recognize a set of six specific static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, preprocessing, feature extraction, and classification. In preprocessing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the first method, the hand contour is used as a feature which treats scaling and translation of problems (in some cases). The complex moment algorithm is, however, used to describe the hand gesture and treat the rotation problem in addition to the scaling and translation. The algorithm used in a multi-layer neural network classifier which uses back-propagation learning algorithm. The results show that the first method has a performance of 70.83% recognition, while the second method, proposed in this article, has a better performance of 86.38% recognition rate.

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References

  • Abd Alrazak R (2004) Signature recognition using wavelet transform. Ph.D. Thesis, University of Technology

  • Abo-Mustafa Y, Psaltis D (1984) Aspects of moment invariants. IEEE Trans Pattern Anal Mach Intell 6(6): 698–706

    Article  Google Scholar 

  • Aditya A, Namrata V, Santanu C, Subhashis B (2003) Recognition of dynamic hand gestures. J Pattern Recogn Soc 36: 12

    MATH  Google Scholar 

  • Awcock GJ, Thomas R (1995) Applied image processing. Mcmillan Press LTD, London

    Book  Google Scholar 

  • Barczak A, Dadgostar F (2005) Real-time hand tracking using a set of cooperative classifiers based on Haar-like features. Res Lett Inf Math Sci 7: 29–42

    Google Scholar 

  • Boussaid K, Beghdaddi A (1999) A contour detection method based on some knowledge of the visual system mechanisms, vision interface. Rrois-Rivieres, Canada

  • Chang CC, Chen JJ, Tai W, Han CC (2006) New approach for static gesture recognition. J Inform Sci Eng 22: 1047–1057

    Google Scholar 

  • Dadgostar F, Barczak ALC, Sarrafzadeh A (2005) A color hand gesture database for evaluating and improving algorithms on hand gesture and posture recognition. In: Research letters in the information and mathematical sciences, vol 7, pp 127–134

  • Fausett L (1994) Fundamentals of neural network’s architectures, algorithms and applications. Prentice Hall Inc, NJ

    MATH  Google Scholar 

  • Freeman WT, Roth M (1995) Orientation Histograms for hand gesture recognition. In: IEEE international workshop on automatic face and gesture recognition, Zurich

  • Gesture (2007) Internet document from http://en.Wikipedia.org/wiki/Gesture

  • Gestures (2007) Internet document from http://user.cs.tu-berlin.de/kai/diplom/gesture.htm

  • Gonzalez RC, Woods RE (2001) Digital image processing. 2nd edn. Wesley Publishing, Cambridge

    Google Scholar 

  • Guiar P (2002) Gesture recognition. Report

  • Guo D, Yan YH, Xie M, (1998) Vision-based Hand gesture recognition for human-vehicle interaction. In: Fifth international conference on control, automation, robotics and vision, Singapore, vol 1

  • Haykin S (1994) Neural networks a comprehensive foundation. Macmillan College Publishing Company, New York

    MATH  Google Scholar 

  • Hu C, Qinghu M, Liu P, Wang X (October 2003) Visual gesture recognition for human–machine interface of robot teleoperation. In: Conference on intelligent robots and systems, Las Vegas

  • Huang J (1998) Spatial image indexing and application. Ph.D thesis, Cornell University

  • Ionescu B, Coquin D, Lambert P, Buzuloiu V (2005) Dynamic hand gesture recognition using the skeleton of the hand. Eurasip J Appl Signal Process 13: 2101–2109

    Article  Google Scholar 

  • Joshi G, Sivaswamy J (2004) A simple scheme for contour detection, Center for Visual Information Technology, IIIT, Hyderabad

  • Just A (2006) Two-handed gestures for human–computer interaction. Ph.D. thesis

  • Kim TK, Cipolla R (2007) Gesture recognition under small sample size. Proc Eighth Asian conf Computer Vision, pp 335–344

  • Kinnebrock W (1995) Neural networks, fundamentals, applications, examples, second revised edition. Galgotia Publications, New Delhi

    Google Scholar 

  • Kjrlfdrn FCM (1997) Visual interpretation of hand gestures as a practical interface modality. Ph.D. thesis, Columbia University, New York

  • Lamar MV (2001) Hand gesture recognition using T-Comb net a neural network model dedicated to temporal information processing. Ph.D. thesis, Nagoya Institute of Technology, Japan

  • Licsár A, Szirányi T (2002) Hand-gesture based film restoration. Second international workshop on pattern recognition in information systems, Alicante, Spain 2002, pp 95–103

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

  • Musa AK (1998) Signature recognition and verification by using complex-moments characteristics. Masters thesis, University of Baghdad

  • Naidoo S, Omlin CW, Glaser M (1998) Vision-based static hand gesture recognition using support vector machines. University of Western Cape, Bellville

    Google Scholar 

  • Nehaniv CI, Dautenhahn K, Kubacki j, Haegele M, Parlitz C (2005) A methodological approach relating the classification of gesture to identification of human intent in the context of human-robot interaction. In: IEEE international workshop on robots and human interactive communication

  • Parvini F, Shahabi C (2007) An algorithmic approach for static and dynamic gesture recognition utilising mechanical and biomechanical characteristics. Int J Bioinform Res Appl 3(1)

  • Philips D (1994) Image pocessing. C, R and D Publications Inc., Lawrence

  • Picton P (2000) Neural networks. 2nd edn. Palgrave, New York

    Google Scholar 

  • Pitas I (1998) Digital image processing algorithms and application. Ph.D thesis, Cornell University

  • Shet VD (2003) Multiple cues, decoupled, exemplar based model, for gesture recognition. Masters thesis, University of Maryland, College Park

  • Symonidis K (2000) Hand gesture recognition using neural networks, vol 68, p 5

  • Triesch J, von der Malsburg C (1996) Robust classification of hand postures against complex backgrounds. In: Proceedings of second international conference on automatic face and gesture recognition, Killington

  • Umbaugh SE (1998) Computer vision and image processing a practical approach using CVIP tools. Prentice Hall, NJ

    Google Scholar 

  • Winnemöller H (1999) Practical gesture recognition for controlling virtual environments. Project for Bachelor of Science (Honours) of Rhodes University

  • Wu Y, Huang TS (1999) Vision-based gesture recognition: a review. International Gesture Workshop, France

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Authors and Affiliations

  1. Faculty of Computer Science and Information of Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Haitham Hasan & S. Abdul-Kareem

Authors
  1. Haitham Hasan
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  2. S. Abdul-Kareem
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Corresponding author

Correspondence to Haitham Hasan.

Additional information

The Editor-in-Chief and the publisher retract the above-mentioned article per the Committee on Publication Ethics (COPE) guidelines on self-plagiarism. The article has significant overlap with two other publications by the same coauthor:

Haitham Sabah Hasan, Sameem Binti Abdul Kareem, Gesture Feature Extraction for Static Gesture Recognition, Arabian J. for Science and Engineering (2013) 38:12. doi:10.1007/s13369-013-0654-6

Haitham Badi, Sabah Hasan Hussein, Sameem Abdul Kareem, Feature Extraction and ML Techniques for Static Gesture Recognition, Neural Computing and Applications (2014) 25:3. doi:10.1007/s00521-013-1540-6

An erratum to this article is available at http://dx.doi.org/10.1007/s10462-017-9544-8.

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Hasan, H., Abdul-Kareem, S. RETRACTED ARTICLE: Static hand gesture recognition using neural networks. Artif Intell Rev 41, 147–181 (2014). https://doi.org/10.1007/s10462-011-9303-1

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  • DOI: https://doi.org/10.1007/s10462-011-9303-1

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