Ensemble Learning Based Gender Recognition from Physiological Signals
文献类型：会议论文
| 作者 | Zhang, Huiling; Guo, Ning; Liu, Guangyuan; Hu, Junhao; Zhou, Jiaxiu; Feng, Shengzhong; Wei, Yanjie |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 英文摘要 | Gender recognition based on facial image, body gesture and speech has been widely studied. In this paper, we propose a gender recognition approach based on four different types of physiological signals, namely, electrocardiogram (ECG), electromyogram (EMG), respiratory (RSP) and galvanic skin response (GSR). The core steps of the experiment consist of data collection, feature extraction and feature selection & classification. We developed a wrapper method based on Adaboost and sequential backward selection for feature selection and classification. Through the data analysis of 234 participants, we obtained a recognition accuracy of 91.1% with a subset of 12 features from ECG/EMG/RSP/GSR, 82.3% with 11 features from ECG only, 80.8% with 5 features from RSP only, indicating the effectiveness of the proposed method. The ECG, EMG, RSP, GSR signals are collected from human wrist, face, chest and fingers respectively, hence the method proposed in this paper can be easily applied to wearable devices. |
| 语种 | 英语 |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14064]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Zhang, Huiling,Guo, Ning,Liu, Guangyuan,et al. Ensemble Learning Based Gender Recognition from Physiological Signals[C]. 见:. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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