Frontal view gait recognition using locally linear embedded and multilayer perceptron based on Kinect
Optimization of gait features using Locally Linear Embedded with Multi-layer Perceptron for frontal view is explored in this study. Static gait features within a gait cycle are extracted from gait data extracted using Kinect. The extracted features are further optimized using Locally Linear Embedded...
| Published in: | Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017 |
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| Main Author: | |
| Format: | Conference paper |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2017
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034810996&doi=10.1109%2fCSPA.2017.8064970&partnerID=40&md5=35f835f12ab037b2813f105696413f88 |
| Summary: | Optimization of gait features using Locally Linear Embedded with Multi-layer Perceptron for frontal view is explored in this study. Static gait features within a gait cycle are extracted from gait data extracted using Kinect. The extracted features are further optimized using Locally Linear Embedded and classified using Multi-layer Perceptron. To verify the effectiveness of the proposed method, original features are also utilised. Result showed that the recognition of human gait using Multi-layer Perceptron with 30 hidden units along with optimal feature of Locally Linear Embedded with K = 88 and d = 64 outshined the recognition rate specifically 98%. © 2017 IEEE. |
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| ISSN: | |
| DOI: | 10.1109/CSPA.2017.8064970 |