Untouchable and Cancelable Biometrics: Human Identification in Various Physiological States Using Radar-Based Heart Signals

Biometric data are extensively used in modern healthcare systems and is often transmitted over networks for various purposes, raising inherent privacy and security concerns. Wearable devices, smartphones, and Internet of Things (IoT) technologies are common sources of such data, which are susceptible to interception during transmission. To mitigate these risks, cancelable biometrics offer a promising solution by enabling secure and privacy-preserving identification. In this study, we propose a cancelable identification model based on contactless heart signals acquired via continuous-wave radar. The recorded signal, which reflects cardiac motion, is first transformed into a scalogram. Feature extraction is then performed using Convolutional Neural Networks (CNNs), comparing models trained via transfer learning with those trained solely on the dataset. Before classification, the extracted features are converted into cancelable templates using Gaussian Random Projection (GRP), and classification is performed using a Multilayer Perceptron (MLP). The proposed method demonstrates feasibility, achieving 91.20% accuracy across all scenarios in the dataset, which increases to 95.40% when focusing solely on the resting scenario. Additionally, CNNs trained exclusively on the dataset outperform pre-trained models using transfer learning in feature extraction performance.