PPG-based Personalized Verification System

Hwang, Dae Yon; Hatzinakos, Dimitrios

doi:10.1109/ccece43985.2019.9052394

Cited by 15 publications

(10 citation statements)

References 6 publications

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“…From the current research on PPG biometric recognition, several methods use their datasets [1,2,4,5,7,12], while others use, e.g., OpenSignal [9], Biosec [9,16], and TROIKA [6,14,15], yet the BIDMC and MIMIC datasets are not involved in this literature. erefore, comparisons between our method and other state-of-the-art methods on the CapnoBase dataset are summarized in Table 6.…”

Section: Comparisons With the State-of-the-art Methodsmentioning

confidence: 99%

“…It indicates that k-NN has advantages for small-scale data. In addition, small-scale data were used in the literature [14][15][16], and the corresponding recognition rate was only 96%, which showed that the performance of deep learning drops apparently on small-scale data. Furthermore, additional experiments were conducted to analyze the influence of data length in feature extraction.…”

Section: Performance Ofmentioning

confidence: 99%

“…Biswas et al [15] presented a novel deep-learning framework (CorNET) to efficiently estimate heart rate (HR) information and performed biometric identification using only wristworn, single-channel PPG signals collected in an ambulant environment, and an average accuracy of 96% was achieved for 20 subjects on the TROIKA dataset. Hwang and Hatzinakos [16] proposed a novel deep-learning-based verification model using PPG signals, built a personalized datadriven network by employing a CNN with LSTM, and modeled the time-series sequence inherent within the PPG signal.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor

Yang

Huang

et al. 2020

Journal of Electrical and Computer Engineering

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Photoplethysmography (PPG) biometric recognition has recently received considerable attention and is considered to be a promising biometric trait. Although some promising results on PPG biometric recognition have been reported, challenges in noise sensitivity and poor robustness remain. To address these issues, a PPG biometric recognition framework is presented in this article, that is, a PPG biometric recognition model based on a sparse softmax vector and k-nearest neighbor. First, raw PPG data are rerepresented by sliding window scanning. Second, three-layer features are extracted, and the features of each layer are represented by a sparse softmax vector. In the first layer, the features are extracted by PPG data as a whole. In the second layer, all the PPG data are divided into four subregions, then four subfeatures are generated by extracting features from the four subregions, and finally, the four subfeatures are averaged as the second layer features. In the third layer, all the PPG data are divided into 16 subregions, then 16 subfeatures are generated by extracting features from the 16 subregions, and finally, the 16 subfeatures are averaged as the third layer features. Finally, the features with first, second, and third layers are combined into three-layer features. Extensive experiments were conducted on three PPG datasets, and it was found that the proposed method can achieve a recognition rate of 99.95%, 97.21%, and 99.92% on the respective sets. The results demonstrate that the proposed method can outperform current state-of-the-art methods in terms of accuracy.

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Section: Comparisons With the State-of-the-art Methodsmentioning

confidence: 99%

Section: Performance Ofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor

Yang

Huang

et al. 2020

Journal of Electrical and Computer Engineering

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“…However, the PPG signals are time-series data, and the information of time dimension is valuable. Therefore, some researchers have added LSTM after CNN to capture long time contextual information [77], [88], [89], [9], [90]. LSTM is a variant of Recurrent Neural Network (RNN).…”

Section: Authentication Modelmentioning

confidence: 99%

SoK: An Overview of PPG's Application in Authentication

Li¹,

Chen²,

Pan³

et al. 2022

Preprint

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Biometric authentication prospered during the 2010s. Vulnerability to spoofing attacks remains an inherent problem with traditional biometrics. Recently, unobservable physiological signals (e.g., Electroencephalography, Photoplethysmography, Electrocardiography) as biometrics have been considered a potential solution to this problem. In particular, Photoplethysmography (PPG) measures the change of blood flow of the human body by an optical method. Clinically, researchers commonly use PPG signals to obtain patients' blood oxygen saturation, heart rate, and other information to assist in diagnosing heart-related diseases. Since PPG signals are easy to obtain and contain a wealth of individual cardiac information, researchers have begun to explore its potential applications in information security. The unique advantages (simple acquisition, difficult to steal, and live detection) of the PPG signal allow it to improve the security and usability of the authentication in various aspects. However, the research on PPG-based authentication is still in its infancy. The lack of systematization hinders new research in this field. We conduct a comprehensive study of PPGbased authentication and discuss these applications' limitations before pointing out future research directions.

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“…Some of them relied on the signal fundamental characteristics to extract features [8] while others used non-fiducial features through wavelet transform [9]. Lately, researchers focused on learned methods from deep learning models [10,11]. The main drawback of the current systems is the impracticality in real life because of the low accuracy or the lack of robustness.…”

Section: Related Workmentioning

confidence: 99%

Variation-Stable Fusion for PPG-Based Biometric System

Hwang

Taha

Hatzinakos

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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This paper investigates the employment of photoplethysmography (PPG) for user authentication systems. Time-stable and user-specific features are developed by stretching the signal, designing a convolutional neural network and performing a variation-stable approach with three score fusions. Two evaluation scenarios are explored, namely single-session and twosessions. In the earlier, the training and testing are done solely on one session data to find the user-specific features, while the second scenario is performed on data from two different sessions to test the time permanence of the features. The verification system was tested on four databases achieving an accuracy of 100% for single-session and 87.3% for two-sessions cases. The simulation results confirm the effectiveness of proposed variation-stable fusion which can be extended to other biometrics. The code is available in [1].

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PPG-based Personalized Verification System

Cited by 15 publications

References 6 publications

Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor

Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor

SoK: An Overview of PPG's Application in Authentication

Variation-Stable Fusion for PPG-Based Biometric System

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