Acceleration plethysmogram based biometric identification

Jaafar, Nur Azua Liyana; Sidek, Khairul Azami; Azam, Siti Nurfarah Ain Mohd

doi:10.1109/icbaps.2015.7292210

Cited by 16 publications

(8 citation statements)

References 10 publications

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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%

“…In the literature, there are several methods which differ one to each other according to the extracted features and to the used classifier. For example, the method described in [14] considers acceleration-based features computed using the derivative of the PPG signal, then applies a Bayes Network and a k-NN classifier. Similarly, the method proposed in [5] extracts features related to the derivative and frequency of the PPG signal, then applies a Linear Discriminant Analysis for classification.…”

Section: Related Workmentioning

confidence: 99%

“…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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…In order to solve this problem, Sammik [4] and Jaagar [5] used APG signals in experiments with 15 and 10 people, respectively, using the Fiducial method, and obtained accuracy results of 100% and 97.5%, respectively. However, since these data sets were small, further research is required in a real environment.…”

Section: Setting Optimal Parameters For Machine Learningmentioning

confidence: 99%

“…In order to compensate for this, previous studies have been conducted second derivatives of PPG signal in order to obtain features using APG signals [4]- [6]. Jaafar et al [5] used features extracted from APG signals in order to provide a 97.5% result for personal authentication using a Bayes network and a K nearest neighbor classification technique.…”

Section: Introductionmentioning

confidence: 99%

Wearable Bio-Signal(PPG)-Based Personal Authentication Method Using Random Forest and Period Setting Considering the Feature of PPG Signals

Lee¹

2019

JCP

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A study regarding personal authentication based on PPG has been conducted using the Random Forest algorithm. In order to ensure correct authentication, data features must be consistent. This consistency is provided through the normalization of the PPG signal using maximum-minimum normalization and spline interpolation. The threshold is set by using normalized data and the highest value among the points above the threshold is set as the peak of the period. After establishing candidates of valley with values below the threshold, a shifted period is calculated by designating the last valley next to the following peak to the valley of the period. In order to reduce errors in the one period PPG data, the data is averaged after overlapping. A discrete cosine transform (DCT) is used to extract features from the preprocessed data. Thus, the extracted features are used as the input variables for machine learning techniques such as Decision Tree, KNN, Random Forest. The accuracy of these algorithms is 93%, 98%, and 99% respectively.

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Statistical Analysis of Derivatives of Cranial Photoplethysmogram in Young Adults

Shriram

Martin

Sundhararajan

et al. 2018

Proceedings of the Second International Conference on Computational Intelligence and Informatics

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Acceleration plethysmogram based biometric identification

Abstract: This paper presents the feasibility study of Acceleration Plethysmogram (APG) based biometric identification system.

Cited by 16 publications

References 10 publications

Biometric Recognition of PPG Cardiac Signals Using Transformed Spectrogram Images

Biometric Recognition of PPG Cardiac Signals Using Transformed Spectrogram Images

Wearable Bio-Signal(PPG)-Based Personal Authentication Method Using Random Forest and Period Setting Considering the Feature of PPG Signals

Statistical Analysis of Derivatives of Cranial Photoplethysmogram in Young Adults

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