An efficient algorithm for 3D hand gesture recognition using combined neural classifiers

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.

[1]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[2]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[3]  Wu-Chih Hu,et al.  Gabor filter-based hand-pose angle estimation for hand gesture recognition under varying illumination , 2011, Expert Syst. Appl..

[4]  Adam Roman,et al.  CORES: fusion of supervised and unsupervised training methods for a multi-class classification problem , 2011, Pattern Analysis and Applications.

[5]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[6]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Mubarak Shah,et al.  Visual gesture recognition , 1994 .

[8]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[9]  Luis Magdalena,et al.  A selection approach for scalable fuzzy integral combination , 2010, Inf. Fusion.

[10]  Alexander E. Kostin,et al.  A simple and fast multi-class piecewise linear pattern classifier , 2006, Pattern Recognit..

[11]  David G. Stork,et al.  Pattern Classification , 1973 .

[12]  Günther Eibl,et al.  Multiclass Boosting for Weak Classifiers , 2005, J. Mach. Learn. Res..

[13]  Terry Windeatt,et al.  Accuracy/Diversity and Ensemble MLP Classifier Design , 2006, IEEE Transactions on Neural Networks.

[14]  Michael J. Black,et al.  A Probabilistic Framework for Matching Temporal Trajectories: CONDENSATION-Based Recognition of Gestures and Expressions , 1998, ECCV.

[15]  Verónica Bolón-Canedo,et al.  An ensemble of filters and classifiers for microarray data classification , 2012, Pattern Recognit..

[16]  Michael R. Lyu,et al.  Face recognition committee machines: dynamic vs. static structures , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..

[17]  Nando de Freitas,et al.  Sequential Monte Carlo in Practice , 2001 .

[18]  Reza Ebrahimpour,et al.  Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels , 2012, Expert Syst. Appl..

[19]  Fabio Roli,et al.  Design of effective neural network ensembles for image classification purposes , 2001, Image Vis. Comput..

[20]  Cheng-Lin Liu,et al.  Classifier combination based on confidence transformation , 2005, Pattern Recognit..

[21]  Steve J. Young,et al.  HMM-based architecture for face identification , 1994, Image Vis. Comput..

[22]  Galina L. Rogova,et al.  Combining the results of several neural network classifiers , 1994, Neural Networks.

[23]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[24]  Geoffrey E. Hinton,et al.  Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls , 1997, IEEE Trans. Neural Networks.

[25]  Mohammed Yeasin,et al.  Visual understanding of dynamic hand gestures , 2000, Pattern Recognit..

[26]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[27]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[28]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[29]  Dimitris N. Metaxas,et al.  A Framework for Recognizing the Simultaneous Aspects of American Sign Language , 2001, Comput. Vis. Image Underst..

[30]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[31]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[32]  Geoffrey E. Hinton,et al.  Glove-Talk: a neural network interface between a data-glove and a speech synthesizer , 1993, IEEE Trans. Neural Networks.

[33]  Nicolás García-Pedrajas,et al.  Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts , 2010, Artif. Intell..

[34]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[35]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[36]  Ahmad S. Tolba,et al.  A PARAMETER-BASED COMBINED CLASSIFIER FOR INVARIANT FACE RECOGNITION , 2000, Cybern. Syst..

[37]  Chin-Shyurng Fahn,et al.  A Human-Machine Interaction Technique: Hand Gesture Recognition Based on Hidden Markov Models with Trajectory of Hand Motion , 2011 .

[38]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[39]  M. S. Sahane,et al.  Visual Interpretation Of Hand Gestures For Human Computer Interaction , 2014 .

[40]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[41]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[42]  Thad Starner,et al.  Visual Recognition of American Sign Language Using Hidden Markov Models. , 1995 .

[43]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[44]  Dimitris N. Metaxas,et al.  ASL recognition based on a coupling between HMMs and 3D motion analysis , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[45]  Aaron F. Bobick,et al.  Parametric Hidden Markov Models for Gesture Recognition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[46]  M. Basu,et al.  Gating improves neural network performance , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[47]  Byoung-Tak Zhang,et al.  Building Optimal Committees of Genetic Programs , 2000, PPSN.

[48]  Alex Pentland,et al.  Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[49]  Geoffrey E. Hinton,et al.  Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls , 1997, IEEE Trans. Neural Networks.

[50]  Vladimir Pavlovic,et al.  Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[51]  Yi Lu Murphey,et al.  Multi-class pattern classification using neural networks , 2007, Pattern Recognit..

[52]  Jin-Hyung Kim,et al.  An HMM-Based Threshold Model Approach for Gesture Recognition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[53]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[54]  Francisco Herrera,et al.  An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes , 2011, Pattern Recognit..

[55]  Edward Hunter,et al.  Vision based hand gesture interpretation using recursive estimation , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

[56]  J. L. Roux.  An Introduction to the Kalman Filter , 2003 .

[57]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[58]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[59]  Yung-Hui Lee,et al.  The parameters effect on performance in ANN for hand gesture recognition system , 2011, Expert Syst. Appl..

[60]  Xin Yao,et al.  Parallel Problem Solving from Nature PPSN VI , 2000, Lecture Notes in Computer Science.

[61]  Junji Yamato,et al.  Recognizing human action in time-sequential images using hidden Markov model , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[62]  L. Breiman.  Arcing classifier (with discussion and a rejoinder by the author) , 1998 .

[63]  Jeffrey Mark Siskind,et al.  A Maximum-Likelihood Approach to Visual Event Classification , 1996, ECCV.

[64]  R. Schapire.  The Strength of Weak Learnability , 1990, Machine Learning.

[65]  Dieter Fox,et al.  Real-time particle filters , 2004, Proceedings of the IEEE.

[66]  Noel E. Sharkey,et al.  The "Test and Select" Approach to Ensemble Combination , 2000, Multiple Classifier Systems.

[67]  Aaron F. Bobick,et al.  A State-Based Approach to the Representation and Recognition of Gesture , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[68]  D. Obradovic,et al.  Combining Artificial Neural Nets , 1999, Perspectives in Neural Computing.

[69]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[70]  M. W. Shields,et al.  A theoretical framework for multiple neural network systems , 2008, Neurocomputing.

[71]  Heung-Il Suk,et al.  Hand gesture recognition based on dynamic Bayesian network framework , 2010, Pattern Recognit..

[72]  Hee-Joong Kang.  Combining multiple classifiers based on third-order dependency for handwritten numeral recognition*1 , 2003, Pattern Recognit. Lett..

[73]  Wei Tang,et al.  Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..

[74]  Thomas S. Huang,et al.  Gesture modeling and recognition using finite state machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[75]  Jean-Philippe Thiran,et al.  Information theoretic combination of pattern classifiers , 2010, Pattern Recognit..

[76]  L. Breiman.  Arcing Classifiers , 1998 .

[77]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .