Abstract
Hand gesture recognition provides an alternative way to many devices for human computer interaction. In this work, we have developed a classifier fusion based dynamic free-air hand gesture recognition system to identify the isolated gestures. Different users gesticulate at different speed for the same gesture. Hence, when comparing different samples of the same gesture, variations due to difference in gesturing speed should not contribute to the dissimilarity score. Thus, we have introduced a two-level speed normalization procedure using DTW and Euclidean distance-based techniques. Three features such as ‘orientation between consecutive points’, ‘speed’ and ‘orientation between first and every trajectory points’ were used for the speed normalization. Moreover, in feature extraction stage, 44 features were selected from the existing literatures. Use of total feature set could lead to overfitting, information redundancy and may increase the computational complexity due to higher dimension. Thus, we have tried to overcome this difficulty by selecting optimal set of features using analysis of variance and incremental feature selection techniques. The performance of the system was evaluated using this optimal set of features for different individual classifiers such as ANN, SVM, k-NN and Naïve Bayes. Finally, the decisions of the individual classifiers were combined using classifier fusion model. Based on the experimental results it may be concluded that classifier fusion provides satisfactory results compared to other individual classifiers. An accuracy of 94.78 % was achieved using the classifier fusion technique as compared to baseline CRF (85.07 %) and HCRF (89.91 %) models.
Similar content being viewed by others
References
Roh, M.C., Lee, S.W.: Human gesture recognition using a simplified dynamic Bayesian network. Multimedia Syst. 21, 557–568 (2015)
Sun, X., Wei, Y., Liang, S., Tang, X., Sun, J.: Cascaded hand pose regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, pp. 824–832 (2015)
Hong, C., Yu, J., Tao, D., Wang, M.: Image-based three-dimensional human pose recovery by multi-view locality sensitive sparse retrieval. IEEE Trans Ind Electron 62(6), 3742–3751 (2015)
Locken, A., Hesselmann, T., Pielot, M., Henze, N., Boll, S.: User-centred process for the definition of free-hand gestures applied to controlling music playback. Multimedia Syst. 18(1), 15–31 (2012)
Elmezain, M., Al-Hamadi, A., Appenrodt, J., Michaelis, B.: A hidden markov model-based continuous gesture recognition system for hand motion trajectory. In: Proceedings of the 19th International Conference on Pattern Recognition, pp. 1–4 (2008)
Kao, C.Y., Fahn, C.S.: A human-machine interaction technique: hand gesture recognition based on hidden Markov models with trajectory of hand motion. Procedia Eng 15, 3739–3743 (2011)
Yoon, H.S., Soh, J., Bae, Y.J., Yang, H.S.: Hand gesture recognition using combined features of location, angle and velocity. Pattern Recogn. 34(7), 1491–1501 (2001)
Elmezain, M., Al-Hamadi, A., Michaelis, B.: Hand gesture recognition based on combined features extraction. World Acad Sci Eng Technol 60, 395 (2009)
Bhuyan, M.K., Ghosh, D., Bora, P.K.: Feature extraction from 2D gesture trajectory in dynamic hand gesture recognition. In: Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, pp. 1–6 (2006)
Bhuyan, M.K., Bora, P.K., Ghosh, D.: Trajectory guided recognition of hand gestures having only global motions. Int J Electric Comput Energet Electron Commun Eng 2(9), 753–764 (2008)
Bhuyan, M.K., Kumar, D.A., MacDorman, K.F., Iwahori, Y.: A novel set of features for continuous hand gesture recognition. J Multimodal User Interf 8(4), 333–343 (2014)
Signer, B., Norrie, M.C., Kurmann, U., iGesture: A Java framework for the development and deployment of stroke-based online gesture recognition algorithms. Technical Report TR561, ETH Zurich (2007)
Singha, J., Laskar, R.H.: Self co-articulation detection and trajectory guided recognition for dynamic hand gestures. IET Comput Vis, pp. 1–10 (2015). doi:10.1049/iet-cvi.2014.0432
Maleki, B., Ebrahimnezhad, H.: Intelligent visual mouse system based on hand pose trajectory recognition in video sequences. Multimedia Syst. 21, 581–601 (2015)
Geetha, M., Menon, R., Jayan, S., James, R., Janardhan, G.V.V.: Gesture recognition for American Sign Language with polygon approximation. In: IEEE International Conference on Technology for Education, 4–16 July 2011, Tamil Nadu, India, pp. 241–245
Rubine, G.V.V.: Specifying gestures by example. In: Proceedings of ACM SIGGRAPH’93. 18th International Conference on Computer Graphics and Interactive Techniques, USA, July 1991, pp. 329–337
Xu, D., Wu, X., Chen, Y.L., Xu, Y.: Online dynamic gesture recognition for human robot interaction. J Intell Rob Syst 77(3–4), 583–596 (2014)
Lin, J., Ding, Y.: A temporal hand gesture recognition system based on hog and motion trajectory. Optik-Int J Light Electron Opt 124(24), 6795–6798 (2013)
Bashir, F.I., Khokhar, A.A., Schonfeld, D.: View-invariant motion trajectory-based activity classification and recognition. Multimedia Syst. 12(1), 45–54 (2006)
Ding, C., Yuan, L.F., Guo, S.H., Lin, H., Chen, W.: Identification of mycobacterial membrane proteins and their types using over-represented tripeptide compositions. J. Proteom. 77, 321–328 (2012)
Ding, H., Li, D.: Identification of mitochondrial proteins of malaria parasite using analysis of variance. Amino Acids 47, 329–333 (2015)
Ding, H., Feng, P.M., Chen, W., Lin, H.: Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis. Mol BioSyst 10(8), 2229–2235 (2014)
Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)
Dasarathy, B.V.: Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press, Los Alamitos (1990)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)
Friedman, N., Geiger, D., Goldszmid, M.: Bayesian network classifiers. Mach. Learn. 29(2), 131–163 (1997)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20, 226–239 (1998)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms, 2nd edn. Wiley, New Jersey (2004)
Semwal, V.B., Mondal, K., Nandi, G.C.: Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput. Appl. (2015). doi:10.1007/s00521-015-2089-3
Yu, J., Tao, D., Wang, M.: Adaptive hypergraph learning and its application in image classification. IEEE Trans. Image Process. 21(7), 3262–3272 (2012)
Yu, J., Rui, Y., Tao, D.: Click prediction for web image reranking using multimodal sparse coding. IEEE Trans. Image Process. 23(5), 2014–2019 (2014)
Yang, H.D., Sclaroff, S., Lee, S.W.: Sign language spotting with a threshold model based on conditional random fields. IEEE Trans. Pattern Anal. Mach. Intell. 31(7), 1264–1277 (2009)
Quattoni, A., Wang, S., Morency, L.P., Collins, M., Darrell, T.: Hidden conditional random fields. IEEE Trans. Pattern Anal. Mach. Intell. 29(10), 1848–1852 (2007)
Bouchrika, T., Zaied, M., Jemai, O., Amar, C.B.: Neural solutions to interact with computers by hand gesture recognition. Multimed. Tools Appl. 72(3), 2949–2975 (2014)
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)
Yu, J., Rui, Y., Tang, Y.Y., Tao, D.: High-order distance based multiview stochastic learning in image classification. IEEE Trans. Cybernet. 44(12), 2431–2442 (2014)
Hsu, Y.-L., Chu, C.-L., Tsai, Y.-J., Wang, J.-S.: An Inertial pen with dynamic time warping recognizer for handwriting and gesture recognition. IEEE Sens. J. 15(1), 154–163 (2015)
Acknowledgments
The authors acknowledge the Speech and Image Processing Lab under Department of ECE at National Institute of Technology Silchar, India for providing all necessary facilities to carry out the research work.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by M. Wang.
Rights and permissions
About this article
Cite this article
Singha, J., Laskar, R.H. Hand gesture recognition using two-level speed normalization, feature selection and classifier fusion. Multimedia Systems 23, 499–514 (2017). https://doi.org/10.1007/s00530-016-0510-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00530-016-0510-0