Ensemble Learning Based Gender Recognition from Physiological Signals
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.
KeywordsGender recognition Physiological signal Feature selection Ensemble learning Wearable devices
This work is supported by National Science Foundation of China under grant no. U1435215 and 61433012, Guangdong Provincial Department of Science and Technology under grant No. 2016B090918122, the Science Technology and Innovation Committee of Shenzhen Municipality under grant No. JCYJ20160331190123578, and No. GJHZ20170314154722613, Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund under Grant No. U1501501, and Youth Innovation Promotion Association, CAS to Yanjie Wei.
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