Gait-Based Authentication Using a RGB Camera

Toral-Alvarez, V.; Álvarez-Aparicio, Claudia; Guerrero‐Higueras, Ángel Manuel; Fernández‐Llamas, Camino

doi:10.1007/978-3-030-87872-6_13

Cited by 4 publications

(1 citation statement)

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“…Recently, behavioral biometric authentication has been a popular research topic due to its inimitable property. For example, gait-based approaches [3,4] use wireless signals or cameras to capture the motion posture for authentication. Brain waves [5][6][7][8] are more trusted mechanisms since it is controlled by the human's unique brain.…”

Section: Introductionmentioning

confidence: 99%

SnapUnlock: A Contrastive Learning-Based Contactless Authentication via Heterogeneous Sensors

Chen

Lin

Li³

et al. 2022

Wireless Communications and Mobile Computing

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Contactless authentication is crucial to keep social distance and prevent bacterial infection. However, existing authentication approaches, such as fingerprinting and face recognition, leverage sensors to verify static biometric features. They either increase the probability of indirect infection or inconvenience the users wearing masks. To tackle these problems, we propose a contactless behavioral biometric authentication mechanism that makes use of heterogeneous sensors. We conduct a preliminary study to demonstrate the feasibility of finger snapping as a natural biometric behavior. A prototype-SnapUnlock system was designed and implemented for further real-world evaluation in various environments. SnapUnlock adopts the principle of contrastive-based representation learning to effectively encode the features of heterogeneous readings. With the representations learned, enrolled samples trained with the classifier can achieve superior performances. We extensively evaluate SnapUnlock involving 50 participants in different experimental settings. The results show that SnapUnlock can achieve a 4.2% average false reject rate and 0.73% average false accept rate. Even in a noisy environment, our system performs similar results.

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