Using Weighted SVM for Identifying User fromGait with Smart Phone
文献类型：会议论文
| 作者 | Qingquan Lai; Bangdao Chen; Chengzhong Xu |
| 出版日期 | 2016 |
| 会议名称 | CCBD 2016 |
| 会议地点 | 澳门 |
| 英文摘要 | Abstract—With the development of authentication technology, fingerprint and speech authentication have applied to most smart devices, which means we are stepping into the era of biometricbased authentication. As the stable biological feature, the gait is used to establish the authentication model in many researches. Most of these researches are based on extracting cycles or statistics from the gait data which used as features in the authentication process with simple machine learning algorithm. The approach presented in the paper that extracts frequencyseries features from gait data from acceleration sensor and uses Weighted Support Vector Machine to recognize users. Further, this paper uses the same methodology to perform the experiment,which shows improved performance of 3.5% EER (Equal Error Rate). |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10341]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 作者单位 | 2016 |
| 推荐引用方式 GB/T 7714 | Qingquan Lai,Bangdao Chen,Chengzhong Xu. Using Weighted SVM for Identifying User fromGait with Smart Phone[C]. 见:CCBD 2016. 澳门. |
入库方式： OAI收割
来源：深圳先进技术研究院
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