Kinect-based human gait recognition using locally linear embedded and support vector machine
Recognition of human gait could be performed effectively provided that significant gait features are well extracted along with effective recognition process. Thus, the gait features should be selected or optimized appropriately for optimal accuracy during recognition. Therefore, in this research, op...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2018
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| Online Access: | http://journalarticle.ukm.my/13800/ http://journalarticle.ukm.my/13800/ http://journalarticle.ukm.my/13800/1/14.pdf |
| Summary: | Recognition of human gait could be performed effectively provided that significant gait features are well extracted along with effective recognition process. Thus, the gait features should be selected or optimized appropriately for optimal accuracy during recognition. Therefore, in this research, optimization of gait features for both oblique and frontal view are evaluated for recognition purpose using Locally Linear Embedded (LLE) along with multi-class Support Vector Machine (SVM). Firstly, dynamic gait features for one gait cycle are extracted from each subject’s walking gait that is acquired using Kinect sensor. Next, the extracted gait features were then optimized using LLE known as DG-LLE and further classified by multi-class SVM with Error Correcting Output Code (ECOC) algorithm. Further, to validate the effectiveness of LLE as optimization technique, the proposed method is then compared with another two gait features namely the original gait features known as DG and optimization using Principal Component Analysis labeled as DG-PCA. Results showed that the optimization based on DG-LLE outperformed the other two methods namely DG and DG-PCA for both oblique and frontal views. In addition, DG-LLE method contributed as the highest recognition rate for both frontal and oblique views. Results also confirmed that the accuracy rate for frontal view is higher specifically 98.33% as compared to oblique view with 94.67%. |
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