Dual-Domain Low-Rank Fusion Deep Metric Learning for Off-the-Person ECG Biometrics

Zhu, Guoqiang; Ma, Mingzhu; Huang, Yu-Wen; Wang, Kuikui; Yang, Geng

doi:10.1109/icassp43922.2022.9747122

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…It is important to note that not all studies conducted experiments across all four combinations of intra-and inter-session scenarios. Specifically, while Zhu et al [51] demonstrated commendable performance in the S1 vs S1 setup, they did not furnish results for the intersession settings, preventing us from directly comparing their system in those scenarios. However, for the inter-session setup, our model yielded remarkable results, boasting an EER of 0.51% for S1 vs S2 and 0.48% for S2 vs S1, a substantial improvement over previous results, notably surpassing the outcomes reported by da Silva et al [53] and Jyotishi and Dandapat [55].…”

Section: B Authentication and Identification 1) Results Without The R...mentioning

confidence: 96%

“…It is essential to acknowledge that not all research works have reported results for all four experimental setup combinations. Specifically, Jyotishi et al [50] and Zhu et al [51] limited their reporting to the S1 vs S1 scenario, while Ibtehaz et al [22] exclusively presented inter-session results. Within the intra-session settings, our model achieved 99% of accuracy, outperforming, in general, previous research endeavors.…”

Section: B Authentication and Identification 1) Results Without The R...mentioning

confidence: 99%

“…In evaluating the effectiveness of our approach using the CYBHi dataset, most previous studies have focused on either intra-session experiments or term inter-session experiments. Intra-session experiments involve obtaining training and test samples from a single session [49]- [51], while term intersession experiments involve enrollment and testing on data separated by a brief period [52]. Being a completely different setup from ours, we did not included works using term setups for the comparison analysis reported in Section VI.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Advancing ECG Biometrics Through Vision Transformers: A Confidence-Driven Approach

D’angelis,

Bacco,

Vollero

et al. 2023

IEEE Access

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Over the past two decades, Electrocardiography (ECG) has gained significant momentum in the field of biometrics, offering a compelling alternative for person identity recognition based on physical/biological traits. Its inherent difficulty to be circumvent and its ability to enable liveness detection make it particularly appealing compared to other popular identifiers such as face, fingerprint, and iris. As a result, ECG has garnered attention from the computer vision community working on biometrics applications. We present a novel biometric method for personal recognition that leverages I-lead signals acquired off-theperson. Through fine-tuning a pre-trained Vision Transformer (ViT) model, we achieve remarkable results in recognizing individuals based on a single 2D image of their ECG recording obtained from as little as three heartbeats. Extensive evaluation on the CYBHi database, with enrollment and testing phases separated by a three-month time window, simulating a real-world, long-term identification scenario, demonstrates the robustness of our multiclass approach. Specifically, our system achieves a remarkable single sample-based identification accuracy of over 70% with a pool of 63 individuals, along with an equal error rate of only 0.48% in the 1-vs-1 authentication task. Additionally, we evaluated our approach on the very recent Heartprint database to assess the robustness of our approach with more subjects, larger separation time windows, and continuous training settings, obtaining again remarkable performance with respect to the state-of-the-art. While the promising capabilities of ECG-based biometrics are evident, given various security challenges, using such methods as standalone authentication could raise caution among users. To address this concern and enhance the system's dependability, we introduce a confidence-based rejection rule. Integrating this mechanism improves both identification and authentication performances, while it could also enable the system to detect out-of-database individuals.

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Section: B Authentication and Identification 1) Results Without The R...mentioning

confidence: 96%

Section: B Authentication and Identification 1) Results Without The R...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Advancing ECG Biometrics Through Vision Transformers: A Confidence-Driven Approach

D’angelis,

Bacco,

Vollero

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In a deep learning context, metric learning has been successfully adopted in different acoustic tasks from emotion recognition [7] and medical diagnosis [8] to speaker [9] and acoustic scene classification [10] [11]. Acoustic scenes are usually composed of several different sources (Dog Bark, Street Music and Siren) and acoustic effects (i.e.…”

Section: Introductionmentioning

confidence: 99%

Improving Non-Stationary Acoustic Source Classification with Metric Learning

Zucatelli,

Barioni

2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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In this work, the metric learning is adopted to improve the classification of non-stationary acoustic sources. The proposed strategy aims to overcome the statistical differences that arise from the non-stationary behavior by learning an optimal function that minimizes intra-class and maximizes inter-class distances. A convolutional neural network with metric learning module generates embedded features of reduced size. Several sources with different degrees of non-stationarity are selected for the acoustic source classification task. Experiments demonstrated that the proposed solution outperforms baseline systems for all individual acoustic sources, leading to increments in the average balanced accuracy, ROC and AUC in all scenarios.

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