Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1557-1564 |
| Number of pages | 8 |
| Journal | Neurocomputing |
| Volume | 72 |
| Issue number | 7-9 |
| DOIs | |
| Publication status | Published - 2009 Mar |
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence
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Lee, H., Hong, S., & Kim, E. (2009). Neural network ensemble with probabilistic fusion and its application to gait recognition. Neurocomputing, 72(7-9), 1557-1564. https://doi.org/10.1016/j.neucom.2008.09.009