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.

[1]  Xiaoming Liu,et al.  Demographic Estimation from Face Images: Human vs. Machine Performance , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Yun Fu,et al.  Gender recognition from body , 2008, ACM Multimedia.

[3]  Gang Wang,et al.  Human Identity and Gender Recognition From Gait Sequences With Arbitrary Walking Directions , 2014, IEEE Transactions on Information Forensics and Security.

[4]  José Miguel Buenaposada,et al.  Robust gender recognition by exploiting facial attributes dependencies , 2014, Pattern Recognit. Lett..

[5]  Gaurav Aggarwal,et al.  Speech Feature Extraction for Gender Recognition , 2016 .

[6]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[7]  Shrikanth S. Narayanan,et al.  Automatic speaker age and gender recognition using acoustic and prosodic level information fusion , 2013, Comput. Speech Lang..

[8]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[9]  Tal Hassner,et al.  Age and gender classification using convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[10]  Antitza Dantcheva,et al.  Gender Estimation Based on Smile-Dynamics , 2017, IEEE Transactions on Information Forensics and Security.

[11]  A. Hall,et al.  New Gender-Specific Partition Values for ECG Criteria of Left Ventricular Hypertrophy: Recalibration Against Cardiac MRI , 2004, Hypertension.

[12]  A. Pedotti,et al.  Effects of gender and posture on thoraco-abdominal kinematics during quiet breathing in healthy adults , 2010, Respiratory Physiology & Neurobiology.

[13]  Stefanie Rukavina,et al.  Affective Computing and the Impact of Gender and Age , 2016, PloS one.

[14]  S. Oertelt-Prigione,et al.  A Successful Strategy to Integrate Sex and Gender Medicine into a Newly Developed Medical Curriculum. , 2015, Journal of women's health.

[15]  C. L. Philip Chen,et al.  Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery , 2011, Int. J. Mach. Learn. Cybern..

[16]  Touradj Ebrahimi,et al.  Privacy in mini-drone based video surveillance , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[17]  M. Cesari,et al.  Sex and gender differences in the treatment of Alzheimer's disease: A systematic review of randomized controlled trials , 2017, Pharmacological research.

[18]  D. Steinberg.  CART: Classification and Regression Trees , 2009 .

[19]  Jianfeng Hu,et al.  An approach to EEG-based gender recognition using entropy measurement methods , 2018, Knowl. Based Syst..