A new gait recognition system based on hierarchical fair competition-based parallel genetic algorithm and selective neural network ensemble

The recognition of a person from his or her gait has been a recent focus in computer vision because of its unique advantages such as being non-invasive and human friendly. However, gait recognition is not as reliable an identifier as other biometrics. In this paper, we applied a hierarchical fair competition-based parallel genetic algorithm and a neural network ensemble to the gait recognition problem. A diverse set of potential neural networks are generated to increase the reliability of the gait recognition, not only the best ones. Furthermore, a set of component neural networks is selected to build a gait recognition system such that generalization errors are minimized and negative correlation is maximized. Experiments are carried out with the NLPR and SOTON gait databases and the effectiveness of the proposed method for gait recognition is demonstrated and compared to previous methods.

[1]  Raymond S. T. Lee,et al.  A New Representation for Human Gait Recognition: Motion Silhouettes Image (MSI) , 2006, ICB.

[2]  Sung-Bae Cho,et al.  Pattern recognition with neural networks combined by genetic algorithm , 1999, Fuzzy Sets Syst..

[3]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Witold Pedrycz,et al.  A new selective neural network ensemble with negative correlation , 2012, Applied Intelligence.

[5]  Euntai Kim,et al.  An Efficient Gait Recognition with Backpack Removal , 2009, EURASIP J. Adv. Signal Process..

[6]  Tianjun Ma,et al.  Towards Feature Fusion for Human Identification by Gait , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[7]  Sudeep Sarkar,et al.  Baseline results for the challenge problem of HumanID using gait analysis , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[8]  Euntai Kim,et al.  A noise robust gait representation: Motion energy image , 2009 .

[9]  Mark S. Nixon,et al.  Gait Verification Using Probabilistic Methods , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[10]  Rama Chellappa,et al.  Gait Analysis for Human Identification , 2003, AVBPA.

[11]  Tieniu Tan,et al.  Silhouette Analysis-Based Gait Recognition for Human Identification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  W. Eric L. Grimson,et al.  Gait analysis for recognition and classification , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[13]  Jiwen Lu,et al.  Gait Recognition via Independent Component Analysis Based on Support Vector Machine and Neural Network , 2005, ICNC.

[14]  Euntai Kim,et al.  Neural network ensemble with probabilistic fusion and its application to gait recognition , 2009, Neurocomputing.

[15]  Jeffrey E. Boyd,et al.  Synchronization of oscillations for machine perception of gaits , 2004, Comput. Vis. Image Underst..

[16]  Sung-Kwun Oh,et al.  Identification of fuzzy models using a successive tuning method with a variant identification ratio , 2008, Fuzzy Sets Syst..

[17]  Marc Moonen,et al.  Incorporating the Conditional Speech Presence Probability in Multi-Channel Wiener Filter Based Noise Reduction in Hearing Aids , 2009, EURASIP J. Adv. Signal Process..

[18]  Shi Chen,et al.  Stride History Image: A New Feature Representation for Pedestrian Identification , 2007, 2007 IEEE Workshop on Signal Processing Systems.

[19]  Sung-Kwun Oh,et al.  Structural and parametric design of fuzzy inference systems using hierarchical fair competition-based parallel genetic algorithms and information granulation , 2008, Int. J. Approx. Reason..

[20]  Lisa Gralewski,et al.  Using a tensor framework for the analysis of facial dynamics , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[21]  David Zhang,et al.  Human gait recognition by the fusion of motion and static spatio-temporal templates , 2007, Pattern Recognit..

[22]  Euntai Kim,et al.  Gait recognition using multi-bipolarized contour vector , 2009 .

[23]  Mark S. Nixon,et al.  On a Large Sequence-Based Human Gait Database , 2004 .

[24]  Mark S. Nixon,et al.  What image information is important in silhouette-based gait recognition? , 2004, CVPR 2004.

[25]  Euntai Kim,et al.  An efficient gait recognition based on a selective neural network ensemble , 2008 .

[26]  Sung-Bae Cho,et al.  Evolutionary ensemble of diverse artificial neural networks using speciation , 2008, Neurocomputing.

[27]  Euntai Kim,et al.  A new genetic feature selection with neural network ensemble , 2009, Int. J. Comput. Math..

[28]  Jonathan H. Connell,et al.  A Statistical Approach for Real-time Robust Background Subtrac tion and Shadow Detection , 2014 .