A Deep Learning Approach on Gender and Age Recognition using a Single Inertial Sensor

Extracting human attributes, such as gender and age, from biometrics have received much attention in recent years. Gender and age recognition can provide crucial information for applications such as security, healthcare, and gaming. In this paper, a novel deep learning approach on gender and age recognition using a single inertial sensors is proposed. The proposed approach is tested using the largest available inertial sensor-based gait database with data collected from more than 700 subjects. To demonstrate the robustness and effectiveness of the proposed approach, 10 trials of inter-subject Monte-Carlo cross validation were conducted, and the results show that the proposed approach can achieve an averaged accuracy of 86.6%±2.4% for distinguishing two age groups: teen and adult, and recognizing gender with averaged accuracies of 88.6%±2.5 % and 73.9% ±2.8 % for adults and teens respectively.

[1]  Michael C. Fairhurst,et al.  Enhancing Identity Prediction Using a Novel Approach to Combining Hard- and Soft-Biometric Information , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Pablo A. Tarazaga,et al.  Gender Classification of Walkers via Underfloor Accelerometer Measurements , 2016, IEEE Internet of Things Journal.

[3]  Björn Krüger,et al.  One Small Step for a Man: Estimation of Gender, Age and Height from Recordings of One Step by a Single Inertial Sensor , 2015, Sensors.

[4]  Jean-Luc Dugelay,et al.  Bag of soft biometrics for person identification , 2010, Multimedia Tools and Applications.

[5]  Heitor Silvério Lopes,et al.  Extracting human attributes using a convolutional neural network approach , 2015, Pattern Recognit. Lett..

[6]  Tarek Sayed,et al.  Use of Spatiotemporal Parameters of Gait for Automated Classification of Pedestrian Gender and Age , 2013 .

[7]  Vivek Kanhangad,et al.  Gender classification in smartphones using gait information , 2018, Expert Syst. Appl..

[8]  Ahmed Sharaf Eldin,et al.  A Survey on Behavioral Biometric Authentication on Smartphones , 2017, J. Inf. Secur. Appl..

[9]  Guang-Zhong Yang,et al.  Secure key generation using gait features for Body Sensor Networks , 2017, 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[10]  Tal Hassner,et al.  Age and Gender Estimation of Unfiltered Faces , 2014, IEEE Transactions on Information Forensics and Security.

[11]  Yasushi Makihara,et al.  Gait-based age estimation using a whole-generation gait database , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[12]  Yasushi Makihara,et al.  The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication , 2014, Pattern Recognit..

[13]  Mohammad Hossein Sedaaghi,et al.  A Comparative Study of Gender and Age Classification in Speech Signals , 2009 .

[14]  Xuelong Li,et al.  Gait Components and Their Application to Gender Recognition , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[15]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[16]  N. Hamdy,et al.  Soft and hard biometrics fusion for improved identity verification , 2004, The 2004 47th Midwest Symposium on Circuits and Systems, 2004. MWSCAS '04..