TY - JOUR
T1 - It's All in the Touch
T2 - Authenticating Users with HOST Gestures on Multi-Touch Screen Devices
AU - Wu, Cong
AU - Cao, Hangcheng
AU - Xu, Guowen
AU - Zhou, Chenjie
AU - Sun, Jianfei
AU - Yan, Ran
AU - Liu, Yang
AU - Jiang, Hongbo
PY - 2024/10
Y1 - 2024/10
N2 - As smartphones proliferate, secure and user-friendly authentication methods are increasingly critical. Existing behavioral biometrics, however, are often compromised by behavior variability, leading to poor authentication accuracy and an unsatisfactory user experience. To fill this gap, we propose BIOHOLD, a new robust and reliable user authentication method, fusing finger behavior and hand geometry, captured via a smartphone's multitouch screen during natural holding gestures. It synergistically fuses behavioral and physiological biometrics. In contrast to traditional methods that require restrictive, unnatural user patterns, our approach utilizes a stable, natural gesture for authentication, effectively mitigating behavior variability. It enables one-handed authentication through familiar smartphone-holding and unlocking gestures. During this interaction, hand geometry and behavioral characteristics are recorded for subsequent authentication. We evaluate our method using a dataset collected from 20 subjects, demonstrating its resilience against behavioral variability over time while maintaining a high level of distinctiveness. With only 10 training samples, our method achieves an equal error rate of 3.59%, which improves to 1.25% with 40 training samples. Importantly, our method is resistant to common security threats such as zero-effort attacks, smudge attacks, and shoulder surfing attacks. A usability study confirms the method's high user acceptance, as measured by the system usability score. © 2024 IEEE.
AB - As smartphones proliferate, secure and user-friendly authentication methods are increasingly critical. Existing behavioral biometrics, however, are often compromised by behavior variability, leading to poor authentication accuracy and an unsatisfactory user experience. To fill this gap, we propose BIOHOLD, a new robust and reliable user authentication method, fusing finger behavior and hand geometry, captured via a smartphone's multitouch screen during natural holding gestures. It synergistically fuses behavioral and physiological biometrics. In contrast to traditional methods that require restrictive, unnatural user patterns, our approach utilizes a stable, natural gesture for authentication, effectively mitigating behavior variability. It enables one-handed authentication through familiar smartphone-holding and unlocking gestures. During this interaction, hand geometry and behavioral characteristics are recorded for subsequent authentication. We evaluate our method using a dataset collected from 20 subjects, demonstrating its resilience against behavioral variability over time while maintaining a high level of distinctiveness. With only 10 training samples, our method achieves an equal error rate of 3.59%, which improves to 1.25% with 40 training samples. Importantly, our method is resistant to common security threats such as zero-effort attacks, smudge attacks, and shoulder surfing attacks. A usability study confirms the method's high user acceptance, as measured by the system usability score. © 2024 IEEE.
KW - Authentication
KW - Behavioral sciences
KW - behavioral variability
KW - Biometrics (access control)
KW - Feature extraction
KW - hand geometry
KW - multi-touch screen
KW - Physiology
KW - Security
KW - Thumb
KW - User authentication
UR - http://www.scopus.com/inward/record.url?scp=85187385377&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85187385377&origin=recordpage
U2 - 10.1109/TMC.2024.3371014
DO - 10.1109/TMC.2024.3371014
M3 - RGC 21 - Publication in refereed journal
SN - 1536-1233
VL - 23
SP - 10016
EP - 10030
JO - IEEE Transactions on Mobile Computing
JF - IEEE Transactions on Mobile Computing
IS - 10
ER -