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Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method’s performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings.

Biometric systems store sensitive personal data that need to be highly protected. However, state-of-the-art template protection schemes generally consist of separate processes, inspired by salting, hashing, or encryption, that limit the achievable performance. Moreover, these are inadequate to protect current state-of-the-art biometric models as they rely on end-to-end deep learning methods. After proposing the Secure Triplet Loss, focused on template cancelability, we now reformulate it to address the problem of template linkability. Evaluated on biometric verification with off-the-person electrocardiogram (ECG) and unconstrained face images, the proposed method proves successful in training secure biometric models from scratch and adapting a pretrained model to make it secure. The results show that this new formulation of the Secure Triplet Loss succeeds in optimizing end-to-end deep biometric models to verify template cancelability, non-linkability, and non-invertibility.

Aiming towards increased robustness to noise and variability, this paper proposes a novel method for electrocardiogram-based authentication, based on an endto-end convolutional neural network (CNN). This network was trained either through the transfer of weights after identification training or using triplet loss, both novel for ECG biometrics. These methods were evaluated on three large ECG collections of diverse signal quality, with varying number of training subjects and user enrollment duration, as well as on cross-database application, with or without fine-tuning. The proposed model was able to surpass the state-of-the-art performance results on off-the-person databases, offering 7.86% and 15.37% Equal Error Rate (EER) on UofTDB and CYBHi, respectively, and attained 9.06% EER on the PTB on-the-person database. The results show the proposed model is able to improve the performance of ECG-based authentication, especially with offthe-person signals, and offers state-of-the-art performance in cross-database tests.