TY - GEN
T1 - Body-taps
T2 - 9th IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS 2018
AU - Shukla, Diksha
AU - Wei, Guangcheng
AU - Xue, Donghua
AU - Jin, Zhanpeng
AU - Phoha, Vir V.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - To fulfill the increasing demands on authentication methods on the smart mobile and wearable devices with small form factors and constrained screen displays, we introduce a novel authentication mechanism, Body-Taps, which authenticates a device based on the Tap-Code gestures in the form of hand movements captured through the built-in motion sensors. The Body-Taps require a user to set a TapCode as an unlock code for the device by tapping the device on the set anchor points on his or her own body. The target device is authenticated based on two criterion: (1) the user's knowledge of the set Tap-Code, and (2) the BodyTap gestures measured through the smart device's built-in motion sensors (accelerometer and gyroscope). Our experiments show that the proposed Body-Taps system can achieve an average authentication accuracy over 99.5% on a dataset comprising of 230 Body-Tap samples from 23 subjects, using Random Forest (RF), Neural Network (NNet), and Linear Discriminant Analysis (LDA) classifiers. Our work yields a light-weight, low-cost, and easy-to-use secure authentication system that requires minimal efforts and offers satisfactory usability.
AB - To fulfill the increasing demands on authentication methods on the smart mobile and wearable devices with small form factors and constrained screen displays, we introduce a novel authentication mechanism, Body-Taps, which authenticates a device based on the Tap-Code gestures in the form of hand movements captured through the built-in motion sensors. The Body-Taps require a user to set a TapCode as an unlock code for the device by tapping the device on the set anchor points on his or her own body. The target device is authenticated based on two criterion: (1) the user's knowledge of the set Tap-Code, and (2) the BodyTap gestures measured through the smart device's built-in motion sensors (accelerometer and gyroscope). Our experiments show that the proposed Body-Taps system can achieve an average authentication accuracy over 99.5% on a dataset comprising of 230 Body-Tap samples from 23 subjects, using Random Forest (RF), Neural Network (NNet), and Linear Discriminant Analysis (LDA) classifiers. Our work yields a light-weight, low-cost, and easy-to-use secure authentication system that requires minimal efforts and offers satisfactory usability.
UR - http://www.scopus.com/inward/record.url?scp=85065402227&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065402227&partnerID=8YFLogxK
U2 - 10.1109/BTAS.2018.8698602
DO - 10.1109/BTAS.2018.8698602
M3 - Conference contribution
AN - SCOPUS:85065402227
T3 - 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018
BT - 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 October 2018 through 25 October 2018
ER -