TY - GEN
T1 - Analysis of head and torso movements for authentication
AU - Parimi, Gayathri Manogna
AU - Kundu, Partha Pratim
AU - Phoha, Vir V.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/3/9
Y1 - 2018/3/9
N2 - Wearable computing devices have become increasingly popular and while these devices promise to improve our lives, they come with new challenges. One such device is the Google Glass from which data can be stolen easily as the touch gestures can be intercepted from a head-mounted device. This paper focuses on analyzing and combining two behavioral metrics, namely, head movement (captured through glass) and torso movement (captured through smartphone) to build a continuous authentication system that can be used on Google Glass alone or by pairing it with a smartphone. We performed a correlation analysis among the features on these two metrics and found that very little correlation exists between the features extracted from head and torso movements in most scenarios (set of activities). This led us to combine the two metrics to perform authentication. We built an authentication system using these metrics and compared the performance among different scenarios. We got EER less than 6% when authenticating a user using only the head movements in one scenario whereas the EER is less than 5% when authenticating a user using both head and torso movements in general.
AB - Wearable computing devices have become increasingly popular and while these devices promise to improve our lives, they come with new challenges. One such device is the Google Glass from which data can be stolen easily as the touch gestures can be intercepted from a head-mounted device. This paper focuses on analyzing and combining two behavioral metrics, namely, head movement (captured through glass) and torso movement (captured through smartphone) to build a continuous authentication system that can be used on Google Glass alone or by pairing it with a smartphone. We performed a correlation analysis among the features on these two metrics and found that very little correlation exists between the features extracted from head and torso movements in most scenarios (set of activities). This led us to combine the two metrics to perform authentication. We built an authentication system using these metrics and compared the performance among different scenarios. We got EER less than 6% when authenticating a user using only the head movements in one scenario whereas the EER is less than 5% when authenticating a user using both head and torso movements in general.
UR - http://www.scopus.com/inward/record.url?scp=85049796521&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049796521&partnerID=8YFLogxK
U2 - 10.1109/ISBA.2018.8311460
DO - 10.1109/ISBA.2018.8311460
M3 - Conference contribution
AN - SCOPUS:85049796521
T3 - 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018
SP - 1
EP - 8
BT - 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Identity, Security, and Behavior Analysis, ISBA 2018
Y2 - 11 January 2018 through 12 January 2018
ER -