Evaluation of the Time Stability and Uniqueness in PPG-Based Biometric System

Hwang, Dae Yon; Taha, Bilal; Lee, Da Saem; Hatzinakos, Dimitrios

doi:10.1109/tifs.2020.3006313

Cited by 43 publications

(28 citation statements)

References 37 publications

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“…Also, it can be noticed that variation-stable approach does not affect the performances for the single-session case while it boosts the performances for the two-sessions scenario which is our main focus (covered in 4.2 section). The best performances outperform other recent works which were 1% for PRRB [8], 17.4% for TROIKA [21] and 1% for Biosec1 [17] in avg EER. Thus, we can conclude that our verification system works well even in different databases with various conditions.…”

Section: Single-session Resultsmentioning

confidence: 60%

“…Later, fully connected layer is appended with sigmoid and binary cross entropy is used to give the classification result. The model was inspired and extended from [10,17] while the hyperparameters are tuned according to the performances in all used databases. In addition, L2 regularization and 10-fold cross validation were used to control overfitting.…”

Section: Deep Learning Modelmentioning

confidence: 99%

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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)

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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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Section: Single-session Resultsmentioning

confidence: 60%

Section: Deep Learning Modelmentioning

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)

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“…It is possible to divide the methods for PPG-based biometric recognition into three categories [13]: i) methods based on algoritmic approaches [2,32,31,35,10]; ii) methods based on handcrafted features and machine learning classifiers [14,29,5,18,19,36,33,37,25,22]; iii) methods based on DNNs [17,26,9,1,24,13].…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, recent studies in the literature analyzed and proved the discriminability of PPG signals as biometric traits. Furthermore, empirical evaluations proved that PPG has sufficient stability for short periods of time [13].…”

Section: Introductionmentioning

confidence: 97%

“…The state-of-the-art methods for biometric recognition based on PPG signals can use algorithmic approaches [2,32,31,35,10], machine learning techniques based on hand-crafted features [37,25,22,24,33,18,19,29,5,23,14,36], or DNNs [13,1,17,26,9]. To the best of our knowledge, all the methods in the literature process PPG signals using features directly extracted from one-dimensional representations of the signals.…”

Section: Introductionmentioning

confidence: 99%

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Biometric Recognition of PPG Cardiac Signals Using Transformed Spectrogram Images

Labati

Piuri

Rundo

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

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Nowadays, the number of mobile, wearable, and embedded devices integrating sensors for acquiring cardiac signals is constantly increasing. In particular, plethysmographic (PPG) sensors are widely diffused thanks to their small form factor and limited cost. For example, PPG sensors are used for monitoring cardiac activities in automotive applications and in wearable devices as smartwatches, activity trackers, and wristbands. Recent studies focused on using PPG signals to secure mobile devices by performing biometric recognitions. Although their results are promising, all of these methods process PPG acquisitions as one-dimensional signals. In the literature, feature extraction techniques based on transformations of the spectrogram have been successfully used to increase the accuracy of signal processing techniques designed for other application scenarios. This paper presents a preliminary study on a biometric recognition approach that extracts features from different transformations of the spectrogram of PPG signals and classifies the obtained feature representations using machine learning techniques. To the best of our knowledge, this is the first study in the literature on biometric systems that extracts features from the spectrogram of PPG signals. Furthermore, with respect to most of the state-of-the-art biometric recognition techniques, the proposed approach presents the advantage of not requiring the search of fiducial points, thus reducing the computational complexity and increasing the robustness of the signal preprocessing step. We performed tests using a dataset of samples collected from 42 individuals, obtaining an average classification accuracy of 99.16% for identity verification (FMR of 0.56% at FNMR of 13.50%), and a rank-1 identification error of 7.24% for identification. The results obtained for the considered dataset are better or comparable with respect to the ones of the best-performing methods in the literature.

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Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology

Abd Elaziz,

Al‐qaness,

Dahou

et al. 2023

WIREs Data Min & Knowl

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The sixth generation (6G) represents the next evolution in wireless communication technology and is currently under research and development. It is expected to deliver faster speeds, reduced latency, and greater capacity compared to the current 5G wireless technology. 6G is envisioned as a technology capable of establishing a fully data‐driven network, proficient in analyzing and optimizing end‐to‐end behavior and handling massive volumes of real‐time data at rates of up to terabits per second (Tb/s). Moreover, 6G is designed to accommodate an average of 1000+ substantial connections per person over the course of a decade. The concept of a data‐driven network introduces a new service paradigm, which offers fresh opportunities for applications within 6G wireless communication and network design in the future. This paper aims to provide a survey of existing applications of 6G that are based on deep learning techniques. It also explores the potential, essential technologies, scenarios, challenges, and related topics associated with 6G. These aspects are crucial for meeting the requirements for the development of future intelligent networks. Furthermore, this work delves into various research gaps between deep learning and 6G that remain unexplored. Different potential deep learning applications for 6G networks, including privacy, security, environmentally friendly communication, sustainability, and various wireless applications, are discussed. Additionally, we shed light on the challenges and future trends in this field.This article is categorized under: Technologies > Computational Intelligence Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

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Evaluation of the Time Stability and Uniqueness in PPG-Based Biometric System

Cited by 43 publications

References 37 publications

Variation-Stable Fusion for PPG-Based Biometric System

Variation-Stable Fusion for PPG-Based Biometric System

Biometric Recognition of PPG Cardiac Signals Using Transformed Spectrogram Images

Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology

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