A novel feature ranking algorithm for biometric recognition with PPG signals

Kavsaoğlu, Ahmet Reşit; Polat, Kemal; Bozkurt, Mehmet Recep

doi:10.1016/j.compbiomed.2014.03.005

Cited by 138 publications

(50 citation statements)

References 22 publications

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“…Additionally, the biometrics (e.g., iris, face, palmprint) captured in the aforementioned vision-based solutions are all external features of human beings, which can be forged by an adversary for launching spoofing attacks [17,18,49]. To overcome the aforementioned weaknesses, some studies rely on intrinsic cardiac biometrics (e.g., heartbeat patterns) derived from electrocardiogram (ECG) [11,20,45,55] and photoplethysmography (PPG) [25] signals. However, these methods require the users to attach specialized sensors to their chest or fingertip, making them hard to be applied to mobile users.…”

Section: Related Workmentioning

confidence: 99%

“…These lead to a renewed search of a low-cost, general, hard-to-forge security solution, which could also facilitate the usage of increasingly convenient mobile payment systems. Existing studies have demonstrated that using either body-attached PPG/ECG sensors [8,12,25,42] or Doppler radar [30] is promising to perform user verification by capturing human cardiac biometrics. These existing investigations usually require specialized equipments (e.g., sensors or radar devices), which could add extra cost and bring inconvenience the mobile users.…”

Section: Introductionmentioning

confidence: 99%

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CardioCam

Liu

Shi

Chen

et al. 2019

Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services

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With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradientbased technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over 99% average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%. * Both authors contributed equally to this research.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CardioCam

Liu

Shi

Chen

et al. 2019

Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services

View full text Add to dashboard Cite

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“…Anyhow there are some drawbacks in PSO algorithm with optimization. Reşit Kavsaoğlu et al [Reşit Kavsaoğlu, Polat and Recep Bozkurt (2014)] intended in utilizing Photoplethysmography (PPG) signal and the time signals for biometric authentication.…”

Section: Related Workmentioning

confidence: 99%

Analysis of OSA Syndrome from PPG Signal Using CART-PSO Classifier with Time Domain and Frequency Domain Features

Sunil¹,

Sankaragomathi²

2019

Computer Modeling in Engineering &Amp; Sciences

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Obstructive Sleep Apnea (OSA) is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation. The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea (SA) activity. In the proposed method, the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted. These features are applied to the Classification and Regression Tree (CART)-Particle Swarm Optimization (PSO) classifier which classifies the signal into normal breathing signal and sleep apnea signal. The proposed method is validated to measure the performance metrics like sensitivity, specificity, accuracy and F1 score by applying time domain and frequency domain features separately. Additionally, the performance of the CART-PSO (CPSO) classification algorithm is evaluated through comparing its measures with existing classification algorithms. Concurrently, the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.

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“…Kavsaoğlu et al proposed a PPG biometric recognition model using a feature‐ranking algorithm. In their experiment, PPG signals were obtained from 30 subjects using PPG data acquisition card.…”

Section: Emerging Biometric Technologiesmentioning

confidence: 99%

State of the art and perspectives on traditional and emerging biometrics: A survey

Traoré

Alshahrani

Obaidat

2018

Security and Privacy

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The last three decades have seen a shift and impressive progress in the biometric technologies landscape. Several major real‐world applications of biometrics have been released and are currently being used around the world. At the same time, several new biometrics modalities have emerged and have started making an impact in securing sensitive data, information, and systems. We present in this paper a survey of traditional and emerging biometrics by emphasizing their connection to human body parts, behavior, and cognition. A review of the state of the art in the key technologies is carried out along with a discussion of major applications currently available in the real world.

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A novel feature ranking algorithm for biometric recognition with PPG signals

Cited by 138 publications

References 22 publications

CardioCam

CardioCam

Analysis of OSA Syndrome from PPG Signal Using CART-PSO Classifier with Time Domain and Frequency Domain Features

State of the art and perspectives on traditional and emerging biometrics: A survey

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