Accelerometer-Based Gait Recognition Using PCA & LDA Algorithms
文献类型：会议论文
| 作者 | Chenfei Mao; Fangmin Sun; Ye Li |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | 英文 |
| 英文摘要 | Abstract—As an emerging biometric authentication technology, gait recognition has various advantages including noninvasive data collecting, continuous gait monitoring, no need user's interactive operation, etc. This paper presents a new gait feature extraction method which can accurately obtain the gait cycle even if the user walking at various speeds. A feature space was first processed by principal component analysis (PCA) and linear discriminant analysis (LDA) to reduce the dimension of the eigenvector and improve the resolution. Finally, the Euclidean distance was used as the template matching algorithm. Experiments have been done on the public dataset and experiment results show that the proposed method has superior recognition performance. The recognition rate was up to 97.5% when acceleration data collected from five body locations were used together. |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14154]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Chenfei Mao,Fangmin Sun,Ye Li. Accelerometer-Based Gait Recognition Using PCA & LDA Algorithms[C]. 见:. 英文. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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