Study on Techniques of Hand Gesture Recognition

Abstract:

This paper describes the study of techniques used in hand gesture recognition in sign language interpretation. The study is discussed from two aspects: the process of hand gesture recognition and the techniques of feature extraction in hand gesture recognition. The techniques of feature extraction in hand gesture recognition are grouped into five categories: Hidden Markov Model (HMM), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Dynamic Bayesian Network (DBN), and Dynamic Time Warping (DTW). The main ideas and the application of each technique are described in detail.

• Periodical: Applied Mechanics and Materials (Volumes 241-244)
• Pages: 1664-1667
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.241-244.1664
• Online since: December 2012
• Authors: Shou Fang Mi, Ling Hua Li
• Keywords: Feature Extraction, Hand Gesture Recognition, Sign Language

References

[1] Y. Fang, K. Wang, J. Cheng, and H. Lu: A Real-Time Hand Gesture Recognition Method, IEEE International Conference on Multimedia and Expo, Beijing, China, pp.995-998 (2007).

DOI: 10.1109/icme.2007.4284820

[2] N. Liu, B.C. Lovell, and P.J. Kootsookos: Evaluation of HMM Training Algorithms for Letter Hand Gesture Recognition, IEEE International Symposium on Signal Processing and Information Technology, pp.648-651 (2003).

DOI: 10.1109/isspit.2003.1341204

[3] F.S. Chen, C.M. Fu, C.L. Huang: Hand Gesture Recognition Using a Real-Time Tracking Method and Hidden Markov Models, Image and Vision Computing, Vol. 21, pp.745-758 (2003).

DOI: 10.1016/s0262-8856(03)00070-2

[4] O. Rashid, A. Al-Hamadi and B. Michaelis: Utilizing Invariant Descriptors for Finger Spelling American Sign Language Using SVM, Advances in Visual Computing Lecture Notes in Computer Science, Vol. 6453, pp.253-263 (2010).

DOI: 10.1007/978-3-642-17289-2_25

[5] H. Suk, B.K. Sin, S.W. Lee: Hand Gesture Recognition Based on Dynamic Bayesian Network Framework, Pattern Recognition, Vol. 43, pp.3059-3072 (2010).

DOI: 10.1016/j.patcog.2010.03.016

[6] M.M. Zaki, S.I. Shaheen: Sign Language Recognition Using a Combination of New Vision Based Features, Pattern Recognition Letters, Vol. 32, pp.572-577 (2011).

DOI: 10.1016/j.patrec.2010.11.013

[7] A. Licsár and T. Szirányi: Hand Gesture Recognition in Camera-Projector System, Computer Vision in Human-Computer Interaction Lecture Notes in Computer Science, Vol. 3058, pp.83-93 (2004).

DOI: 10.1007/978-3-540-24837-8_9

[8] H. Cooper, E.J. Ong and R. Bowden: Give Me a Sign: A Person Independent Interactive Sign Dictionary, Technical Report VSSP-TR-1 (2011).

[9] S. Marcel, O. Bernier and J.E. Viallet et al.: Hand Gesture Recognition Using Input-Output Hidden Markov Models. Proceeding of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp.456-461 (2000).

DOI: 10.1109/afgr.2000.840674

[10] S. Mitra and T. Acharya: Gesture Recognition: A Survey. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, Vol. 37, pp.311-324 (2007).

DOI: 10.1109/tsmcc.2007.893280

[11] C. Nolker, H. Ritter: Visual Recognition of Continuous Hand Postures, IEEE Transactions on Neural Networks, Vol 13, pp.983-994 (2002).

DOI: 10.1109/tnn.2002.1021898

[12] D. Huang, W. Hu, S. Chang: Gabor Filter-Based Hand-Pose Angle Estimation for Hand Gesture Recognition Under Varying Illumination, Expert Systems with Applications, (2010).

DOI: 10.1016/j.eswa.2010.11.016

[13] H. Sakoe, S. Chiba: Dynamic programming algorithm optimization for spoken word recognition, IEEE Trans Acoustics Speech Signal Process ASSP 26, pp.43-49 (1978).

DOI: 10.1109/tassp.1978.1163055

[14] A. Corradini: Dynamic Time Warping for Off-Line Recognition of a Small Gesture Vocabulary, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, pp.82-89 (2001).

DOI: 10.1109/ratfg.2001.938914