RT info:eu-repo/semantics/article
T1 Deep Learning system for user identification using sensors on doorknobs
A1 Vegas Hernández, Jesús María
A1 Rao, A. Ravishankar
A1 Llamas Bello, César
K1 access control
K1 User identification
K1 IoT
K1 sensors
K1 machine learning
AB Door access control systems are important to protect the security and integrity of physical spaces. Accuracy and speed are important factors that govern their performance. In this paper, we investigate a novel approach to identify users by measuring patterns of their interactions with a doorknob via an embedded accelerometer and gyroscope and by applying deep-learning-based algorithms to these measurements. Our identification results obtained from 47 users show an accuracy of 90.2%. When the sex of the user is used as an input feature, the accuracy is 89.8% in the case of male individuals and 97.0% in the case of female individuals. We study how the accuracy is affected by the sample duration, finding that is its possible to identify users using a sample of 0.5 s with an accuracy of 68.5%. Our results demonstrate the feasibility of using patterns of motor activity to provide access control, thus extending with it the set of alternatives to be considered for behavioral biometrics.
PB MDPI
SN 1424-8220
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/74516
UL https://uvadoc.uva.es/handle/10324/74516
LA eng
NO Sensors, Agosto 2024, vol. 24, n. 15.
NO Producción Científica
DS UVaDOC
RD 09-abr-2025