Continuous Authentication in Resource-Constrained Devices via Biometric and Environmental Fusion
Zeeshan N. Bakyt M. Moradpoor N. La Spada L.
September 2025Multidisciplinary Digital Publishing Institute (MDPI)
Sensors
2025#25Issue 18
Continuous authentication allows devices to keep checking that the active user is still the rightful owner instead of relying on a single login. However, current methods can be tricked by forging faces, revealing personal data, or draining the battery. Additionally, the environment where the user plays a vital role in determining the user’s online security. Thanks to several security attacks, such as impersonation and replay, the user or the device can easily be compromised. We present a lightweight system that pairs face recognition with complex environmental sensing, i.e., the phone validates the user when the surrounding light or noise changes. A convolutional network turns each captured face into a 128-bit code, which is combined with a random “nonce” and protected by hashing. A camera–microphone module monitors light and sound to decide when to sample again, reducing unnecessary checks. We verified the protocol with formal security tools (Scyther v1.1.3.) and confirmed resistance to replay, interception, deepfake, and impersonation attacks. Across 2700 authentication cycles on a Snapdragon 778G testbed, the median decision time decreased from 61.2 ± 3.4 ms to 42.3 ± 2.1 ms (p < 0.01, paired t-test). Data usage per authentication cycle fell by an average of 24.7% ± 1.8%, and mean energy consumption per cycle decreased from 21.3 mJ to 19.8 mJ (∆ = 6.6 mJ, 95% CI: 5.9–7.2). These differences were consistent across varying lighting (≤50, 50–300, >300 lux) and noise conditions (30–55 dB SPL). These results show that smart-sensor-triggered face recognition can offer secure and energy-efficient continuous verification, supporting smart imaging and deep-learning-based face recognition.
adaptive biometrics , continuous authentication , deep learning , edge computing , environmental sensing , facial recognition , lightweight cryptography , smart sensors
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School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, EH10 5DT, United Kingdom
Department of Information Security, Faculty of Information Technology, L.N. Gumilyov, Eurasian National University, Pushkin st., 2, Astana, 010008, Kazakhstan
School of Computing
Department of Information Security
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