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 effectiveness of the proposed method for gait recognition is demonstrated and compared to previous methods.
| Original language | English |
|---|---|
| Pages (from-to) | 202-207 |
| Number of pages | 6 |
| Journal | International Journal of Control, Automation and Systems |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2014 Feb 1 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications
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A new gait recognition system based on hierarchical fair competition-based parallel genetic algorithm and selective neural network ensemble. / Lee, Heesung; Lee, Heejin; Kim, Euntai.
In: International Journal of Control, Automation and Systems, Vol. 12, No. 1, 01.02.2014, p. 202-207.Research output: Contribution to journal › Article
TY - JOUR
T1 - A new gait recognition system based on hierarchical fair competition-based parallel genetic algorithm and selective neural network ensemble
AU - Lee, Heesung
AU - Lee, Heejin
AU - Kim, Euntai
PY - 2014/2/1
Y1 - 2014/2/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84896787566&partnerID=8YFLogxK
U2 - 10.1007/s12555-012-0154-6
DO - 10.1007/s12555-012-0154-6
M3 - Article
AN - SCOPUS:84896787566
VL - 12
SP - 202
EP - 207
JO - International Journal of Control, Automation and Systems
JF - International Journal of Control, Automation and Systems
SN - 1598-6446
IS - 1
ER -