Awareness of heart attack symptoms and the best response is a national public health priority, especially among those at higher risk of heart disease. Adults with sensory loss are more likely to develop heart disease than those without and may be at risk of poor heart attack knowledge owing to limited patient–provider communication and access to health information. The aim of this study is to examine the association between sensory loss and heart attack knowledge.

Vulnerability to presentation attacks (PAs) remains one of the main security concerns of the widely used fingerprint-based authentication systems, especially for unattended and remote applications. PAs can be carried out by presenting artifact, corpse, or conformant samples to a biometric sensor with the intention of circumventing the system policy. In this study, we develop a multilayer biometric authentication system robust against PAs by the fusion of fingerprint and heart-signal. In the first layer, artifact attacks are prevented by using a fine-tuned convolutional neural network (CNN). In the second layer, a lightweight CNN is used for the prevention of corpse attacks by using heart-signal (also known as ECG signal) with duration of 0.5 s. In the subsequent layers, robust fingerprint matcher at a specific threshold are utilized for the prevention of conformant attacks. In the final layer, a score-level fusion of the fingerprint and heart-signal is used for biometric authentication. The proposed system was evaluated by different authentication and attack scenarios using a multimodal dataset comprising two public databases of fingerprints and heart-signals available online. The experimental results yielded a false match rate (FMR) of approximately zero (0.1%) with an acceptable false non-match rate (FNMR). The obtained results are encouraging for the incorporation of the system into applications requiring high-security authentication.

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Mishita
Journal Co-ordinator
Journal of Autoimmune Disorders