Abstract
Gestures are the dynamic movements of hands within a certain time interval, which are of practical importance in many areas, such as human–computer interaction, computer vision, and computer graphics. The human hand gesture can provide a free and natural alternative to today’s cumbersome interface devices so as to improve the efficiency and effectiveness of human–computer interaction. This paper presents a neural-based combined classifier for 3D gesture recognition. The combined classifier is based on varying the parameters related to both the design and training of neural network classifier. The boosting algorithm is used to make perturbation of the training set employing the Multi-Layer Perceptron as base classifier. The final decision of the ensemble of classifiers is based on the majority voting rule. Experiments performed on 3D gesture database show the robustness of the proposed technique.
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Mitra S, Acharya T (2007) Gesture recognition: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 37(3):311–324
Cipolla R, Pentland A (1998) Computer vision for human-machine interaction. Cambridge University Press, Cambridge, MA
Pavlovic VI, Sharma R, Huang TS (1997) Visual interpretation of hand gestures for human–computer interaction. IEEE Trans Pattern Anal Machine Intell 19(7):677–695
Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Machine Intell 3:226–239
Rogova G (1994) Combining the results of several neural network classifiers. Neural Netw 7(5):777–781
Zhou ZH, Wu J, Tang W (2002) Ensembling neural networks: many could be better than all. Artif Intel 137(1–2):239–263
Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley-Interscience
García-Osorio C, de Haro-García A, García-Pedrajas N (2010) Democratic instance selection: a linear complexity instance selection algorithm based on classifier ensemble concepts. Artif Intell 174(5–6):410–441
Galar M, Fernández A, Barrenechea E, Bustince H, Herrera F (2011) An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recogn 44(8):1761–1776
Tabassian M, Ghaderi R, Ebrahimpour R (2012) Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels. Expert Syst Appl 39(2):1698–1707
Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2012) An ensemble of filters and classifiers for microarray data classification. Pattern Recogn 45(1):531–539
Podolak IT, Roman A (2011) CORES: Fusion of supervised and unsupervised training methods for a multi-class classification problem. Pattern Anal Appl 1–19
Diettrich TG (2000) Ensemble methods in machine learning. In: Proceedings of the first international workshop on multiple classifier systems, Cagliari, Italy, June, Lecture Notes in Computer Science, Springer, Berlin, pp 1–15
Breiman L (1997) Bagging predictors. Machine Lear 24(2):123–140
Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139
Giacinto G, Roli F (2001) Design of effective neural network ensembles for image classification. J Image Vis Comp 19(9–10):697–705
Sharkey AJC (1996) On combining artificial neural nets. Connection Sci 8:299–314
Sharkey AJC, Sharkey NE, Gerecke U, Chandroth GO (2000) The test and select approach to ensemble combination. In: Kittler J, Roli F (eds) Proceedings of the first international workshop on multiple classifier systems (MCS2000) LNCS 1857. Springer, pp 30–44
Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. In: Proceedings IEEE 77(2):257–285
Yamato J, Ohya J, Ishii K (1992) Recognizing human action in time sequential images using hidden Markov model. In: Proceedings of IEEE international conference on computer vision pattern recogn. Champaign, IL, pp 379–385
Samaria F, Young S (1994) HMM-based architecture for face identification. Image Vis Comput 12:537–543
Arulapalam S, Maskell S, Gordon N, Clapp T (2001) A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Proc 50(2):174–188
Kwok C, Fox D, Meila M (2004) Real-time particle filters. In: Proceedings IEEE, vol. 92, no. 3, pp 469–484
Welch G, Bishop G (2000) An introduction to the Kalman filter. Department of Computer Science, University of North Carolina, Chapel Hill, Technical Report, TR95041
Isard M, Blake A (1996) Contour tracking by stochastic propagation of conditional density. In: Proceedings of European conference on computer vision, Cambridge, UK pp 343–356
Isard M, Blake A (1998) CONDENSATION—conditional density propagation for visual tracking. Int J Comput Vis 1:5–28
Doucet A, De Freitas N, Gordon N (2001) Sequential monte carlo in practice. Springer, NewYork, NY
Davis J, Shah M (1994) Visual gesture recognition. Vis Image Signal Proc 141:101–106
Bobick AF, Wilson AD (1997) A state-based approach to the representation and recognition of gesture. IEEE Trans Pattern Anal Mach Intell 19(12):1235–1337
Yeasin M, Chaudhuri S (2000) Visual understanding of dynamic hand gestures. Pattern Recogn 33:1805–1817
Hong P, Turk M, Huang TS (2000) Gesture modeling and recognition using finite state machines. In: Proceedings 4th IEEE international conference autom on face gesture recognition, Grenoble, France, pp 410–415
Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York, NY
Gonzalez RC, Woods RE (2008) Digital image processing, 3rd edn. Prentice Hall, Englewood Cliffs NJ
Kass M, Witkin A, Terzopoulos D (1988) SNAKE: active contour models. Int J Comput Vis 321–331
Haykin S (1999) Neural networks: a comprehensive foundation. Macmillan, NewYork, NY
Schlenzig J, Hunter E, Jain R (1994) Vision based hand gesture interpretation using recursive estimation. In: Proceedings 28th asilomar conference on signals, systems, and computers
Starner TE, Pentland A (1995) Visual recognition of American sign language using hidden Markov models. In: Proceedings first int’l workshop automatic face and gesture recognition, pp 189–194
Starnder T, Weaver J, Pentland A (1998) Real-time American sign language recognition using desk and wearable computer based video. IEEE Trans Pattern Anal Machine Intell 20(12):1371–1375
Fels SS, Hinton GE (1993) Glove-talk: a neural network interface between a data-glove and a speech synthesizer. IEEE Trans Neural Netw 4(1):2–8
Fels SS, Hinton GE (1997) Glove-talk II: a neural network interface which maps gestures to parallel format speech synthesizer controls. IEEE Trans Neural Netw 9(1):205–212
Siskind JM, Morris Q (1996) A maximum-likelihood approach to visual event classification. In: Proceedings Fourth European conference on computer vision, pp 347–360
Bobick AF, Wilson AD (1997) A state-based approach to the representation and recognition of gesture. IEEE Trans Pattern Anal Machine Intell 19(12):1325–1337
Wilson AD, Bobick AF (1999) Parametric hidden markov models for gesture recognition. IEEE Trans Pattern Anal Machine Intell 21(9):884–900
Black MJ, Jepson AD (1998) A probabilistic framework for matching temporal trajectories: CONDENSATION-based recognition of gesture and expressions. In: Proceedings fifth European conference on computer vision, pp 909–924
Vogler C, Metaxas D (1998) ASL recognition based on a coupling between HMMs and 3D motion analysis. In: Proceedings sixth IEEE int’l conference on computer vision, pp 363–369
Vogler C, Metaxas D (2001) A framework for recognizing the simultaneous aspects of American sign language. Comput Vis Image Understand 81(3):358–384
Lee HK, Kim JH (1999) An HMM-based threshold model approach for gesture recognition. IEEE Trans Pattern Anal Machine Intell 21(10):961–973
Suk H-I, Sin B-K, Lee S-W (2010) Hand gesture recognition based on dynamic Bayesian network framework. Pattern Recogn 43(9):3059–3072
Kao C-Y, Fahn C-S (2011) A human-machine interaction technique: hand gesture recognition based on hidden markov models with trajectory of hand motion. Procedia Eng 15:3739–3743
Huang D-Y, Hu W-C, Chang S-H (2011) Gabor filter-based hand-pose angle estimation for hand gesture recognition under varying illumination. Expert Syst Appl 38(5):6031–6042
Tsai C-Y, Lee Y-H (2011) The parameters effect on performance in ANN for hand gesture recognition system. Expert Syst Appl 38(7):7980–7983
Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In machine learning. In: Proceedings of the thirteenth international conference, pp 148–15
Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Machine Intell 20(8):832–844
Breiman L (1998) Arcing classifiers. The Annals Stat 26(3):801–849
Eibl G, Pfeiffer K-P (2005) Multiclass boosting for week classifiers. J Machine Learn Res 6:189–210
Schapire RE, Freund Y, Bartlett P, Lee W (1998) Boosting the margin: a new explanation for the effectiveness of voting methods. The Annals Stat 26(5):1651–1686
Friedman J, Hastie T, and Tibshirani R (1998) Additive logistic regression: a statistical view of boosting. Technical Report Technical Report, Stanford University, Department of Statistics
Tolba AS (2000) A parameter-based combined classifier for invariant face recognition. Cybern Syst 31:837–849
Tolba AS, Abu-Rezq AN (2000) Combined classifiers for invariant face recognition. Pattern Anal Appl 3(4):289–302
Su M, Basu M (2001) Gating improves neural network performance. In: Proceedings IEEE conference on IJCNN’01, vol. 3, pp 2159–2164
Zhang BT, Joung JG (2000) Building optimal committees of genetic programs. In: parallel problem solving from nature—PPSN VI, pp 231–240
Liu C-L (2005) Classifier combination based on confidence transformation. Pattern Recogn 38(1):11–28
Kostin A (2006) A simple and fast multi-class piecewise linear pattern classifier. Pattern Recogn 39(11):1949–1962
Ou G, Murphey YL (2007) Multi-class pattern classification using neural networks. Pattern Recogn 40(1):4–18
Shields MW, Matthew C (2008) Casey, theoretical framework for multiple neural network systems. Neurocomputing 71(7–9):1462–1476
Kang H-J (2003) Combining multiple classifiers based on third-order dependency for handwritten numeral recognition. Pattern Recogn Lett 24(16):3027–3036
Meynet J, Thiran JP (2010) Information theoretic combination of pattern classifiers. Pattern Recogn 43(10):3412–3421
Bulacio P, Guillaume S, Tapia E, Magdalena L (2010) A selection approach for scalable fuzzy integral combination. Inf Fus 11(2):208–213
Terry W (2006) Accuracy/diversity and ensemble MLP classifier design. IEEE Trans Neural Netw 17(5):1194–1211
Sheng J (2003) A study of adaboost in 3D gesture recognition. Technical report, Computer Science Department, Toronto University, Toronto, ON
Schapire RE (1990) The strength of weak learnability. Machine Learn 5(2):197–227
Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, Berlin
Hilario M, Model combination. Geneva Lab, cui.unige.ch/AI-group/teaching/dmc/09-10/…/dm11-ensembles.pdf
Tang H-M, Lyu MR, King I (2003) Face recognition committee machines: dynamic versus static structures. ICIAP 121–126
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El-Baz, A.H., Tolba, A.S. An efficient algorithm for 3D hand gesture recognition using combined neural classifiers. Neural Comput & Applic 22, 1477–1484 (2013). https://doi.org/10.1007/s00521-012-0844-2
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DOI: https://doi.org/10.1007/s00521-012-0844-2