TY - JOUR
T1 - Neural network ensemble with probabilistic fusion and its application to gait recognition
AU - Lee, Heesung
AU - Hong, Sungjun
AU - Kim, Euntai
N1 - Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.
PY - 2009/3
Y1 - 2009/3
N2 - The recognition of a person from his (or her) gait is a relatively new and promising research direction in biometrics since it is noninvasive and human friendly. Gait recognition, however, has the weakness that it is not reliable compared with other biometrics. To increase reliability, we applied a neural network ensemble with probabilistic fusion to the gait recognition problem. To improve recognition accuracy, we define belief as the posterior probability of the pattern and combine the component neural networks of the ensemble based on the belief. Experiments are performed with the NLPR and SOTON databases, and the effectiveness of the proposed method for gait recognition is demonstrated.
AB - The recognition of a person from his (or her) gait is a relatively new and promising research direction in biometrics since it is noninvasive and human friendly. Gait recognition, however, has the weakness that it is not reliable compared with other biometrics. To increase reliability, we applied a neural network ensemble with probabilistic fusion to the gait recognition problem. To improve recognition accuracy, we define belief as the posterior probability of the pattern and combine the component neural networks of the ensemble based on the belief. Experiments are performed with the NLPR and SOTON databases, and the effectiveness of the proposed method for gait recognition is demonstrated.
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U2 - 10.1016/j.neucom.2008.09.009
DO - 10.1016/j.neucom.2008.09.009
M3 - Article
AN - SCOPUS:61849145792
VL - 72
SP - 1557
EP - 1564
JO - Review of Economic Dynamics
JF - Review of Economic Dynamics
SN - 1094-2025
IS - 7-9
ER -