Heesung Lee, Heejin Lee, and Euntai Kim*
International Journal of Control, Automation, and Systems, vol. 12, no. 1, pp.202-207, 2014

Abstract : 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 effec-tiveness of the proposed method for gait recognition is demonstrated and compared to previous meth-ods.

Keyword : Gait recognition, hierarchical fair competition-based parallel genetic algorithm, negative correlation, neural network ensemble, selective neural network ensemble.