Abstract
Gait recognition has received increasing attention in biometrics. However, more effort is needed to enhance the performance. In this paper, we investigate a novel descriptor for gait recognition known as Kernel-based Fuzzy Local Binary Pattern (KFLBP). The spatio-Temporal static and dynamic characteristics of a gait sequence is first summarized using a Gait-Energy Image (GEI). Then, the proposed approach combines multiple FLBP with different radii to better handle uncertainty in GEI and improve the recognition performance. We evaluate the proposed method on CASIA B dataset at different view angles. We also compare the performance with other feature extraction methods and explore the impact of different walking covariates on the performance.
Original language | English |
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Title of host publication | Proceedings - UKSim-AMSS 2016 |
Subtitle of host publication | 10th European Modelling Symposium on Computer Modelling and Simulation |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 35-40 |
Number of pages | 6 |
ISBN (Electronic) | 9781509049707 |
DOIs | |
State | Published - 4 May 2017 |
Publication series
Name | Proceedings - UKSim-AMSS 2016: 10th European Modelling Symposium on Computer Modelling and Simulation |
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Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Gait recognition; human motion; image processing; multi-kernel; fuzzy logic; local binary patterns; gait energy image.
ASJC Scopus subject areas
- Computer Networks and Communications
- Artificial Intelligence
- Modeling and Simulation