TY - GEN
T1 - Active Authentication on Smartphone using Touch Pressure
AU - Kudo, Masashi
AU - Yamana, Hayato
N1 - Publisher Copyright:
© 2018 Copyright is held by the owner/author(s).
PY - 2018/10/11
Y1 - 2018/10/11
N2 - Smartphone user authentication is still an open challenge because the balance between both security and usability is indispensable. To balance between them, active authentication is one way to overcome the problem. In this paper, we tackle to improve the accuracy of active authentication by adopting online learning with touch pressure. In recent years, it becomes easy to use the smartphones equipped with pressure sensor so that we have confirmed the effectiveness of adopting the touch pressure as one of the features to authenticate. Our experiments adopting online AROW algorithm with touch pressure show that equal error rate (EER), where the miss rate and false rate are equal, is reduced up to one-fifth by adding touch pressure feature. Moreover, we have confirmed that training with the data from both sitting posture and prone posture archives the best when testing variety of postures including sitting, standing and prone, which achieves EER up to 0.14%.
AB - Smartphone user authentication is still an open challenge because the balance between both security and usability is indispensable. To balance between them, active authentication is one way to overcome the problem. In this paper, we tackle to improve the accuracy of active authentication by adopting online learning with touch pressure. In recent years, it becomes easy to use the smartphones equipped with pressure sensor so that we have confirmed the effectiveness of adopting the touch pressure as one of the features to authenticate. Our experiments adopting online AROW algorithm with touch pressure show that equal error rate (EER), where the miss rate and false rate are equal, is reduced up to one-fifth by adding touch pressure feature. Moreover, we have confirmed that training with the data from both sitting posture and prone posture archives the best when testing variety of postures including sitting, standing and prone, which achieves EER up to 0.14%.
KW - Active authentication
KW - Online learning
KW - Touch pressure
UR - http://www.scopus.com/inward/record.url?scp=85056830274&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056830274&partnerID=8YFLogxK
U2 - 10.1145/3266037.3266113
DO - 10.1145/3266037.3266113
M3 - Conference contribution
AN - SCOPUS:85056830274
T3 - UIST 2018 Adjunct - Adjunct Publication of the 31st Annual ACM Symposium on User Interface Software and Technology
SP - 96
EP - 98
BT - UIST 2018 Adjunct - Adjunct Publication of the 31st Annual ACM Symposium on User Interface Software and Technology
PB - Association for Computing Machinery, Inc
T2 - 31st Annual ACM Symposium on User Interface Software and Technology, UIST 2018
Y2 - 14 October 2018 through 17 October 2018
ER -