Abstract
With the rapid advances in sensor networks and internet-of-things, several applications have evolved to ubiquitously monitor what is happening in smart environments. This paper designs a gait-based gender identification system based on an improved method of fuzzy local binary patterns for texture analysis. The moving person’s silhouette is extracted and a texture image is constructed to summarize structural and dynamical variations over one gait cycle. Then, histograms representing soft pattern variations are constructed and used as feature vectors. The proposed method employs grid search to find the optimal hyper-parameters for the feature extraction method. Finally, support vector machines are trained to predict the gender of the walking person. The performance is evaluated and compared with four other local binary pattern (LBP) related texture descriptors. The results show significant improvements in the performance under different walking conditions.
Original language | English |
---|---|
Pages (from-to) | 2495-2504 |
Number of pages | 10 |
Journal | Journal of Ambient Intelligence and Humanized Computing |
Volume | 10 |
Issue number | 7 |
DOIs | |
State | Published - 1 Jul 2019 |
Bibliographical note
Publisher Copyright:© 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
Keywords
- Gait energy image
- Gender recognition
- Local binary patterns
- Support vector machine
- Surveillance video
- Texture feature extraction
ASJC Scopus subject areas
- General Computer Science