189 research outputs found
Evaluation of PPG Biometrics for Authentication in different states
Amongst all medical biometric traits, Photoplethysmograph (PPG) is the
easiest to acquire. PPG records the blood volume change with just combination
of Light Emitting Diode and Photodiode from any part of the body. With IoT and
smart homes' penetration, PPG recording can easily be integrated with other
vital wearable devices. PPG represents peculiarity of hemodynamics and
cardiovascular system for each individual. This paper presents non-fiducial
method for PPG based biometric authentication. Being a physiological signal,
PPG signal alters with physical/mental stress and time. For robustness, these
variations cannot be ignored. While, most of the previous works focused only on
single session, this paper demonstrates extensive performance evaluation of PPG
biometrics against single session data, different emotions, physical exercise
and time-lapse using Continuous Wavelet Transform (CWT) and Direct Linear
Discriminant Analysis (DLDA). When evaluated on different states and datasets,
equal error rate (EER) of - was achieved for -s average
training time. Our CWT/DLDA based technique outperformed all other
dimensionality reduction techniques and previous work.Comment: Accepted at 11th IAPR/IEEE International Conference on Biometrics,
2018. 6 pages, 6 figure
CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping
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 gradient-based 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%
H2B: Heartbeat-based Secret Key Generation Using Piezo Vibration Sensors
We present Heartbeats-2-Bits (H2B), which is a system for securely pairing
wearable devices by generating a shared secret key from the skin vibrations
caused by heartbeat. This work is motivated by potential power saving
opportunity arising from the fact that heartbeat intervals can be detected
energy-efficiently using inexpensive and power-efficient piezo sensors, which
obviates the need to employ complex heartbeat monitors such as
Electrocardiogram or Photoplethysmogram. Indeed, our experiments show that
piezo sensors can measure heartbeat intervals on many different body locations
including chest, wrist, waist, neck and ankle. Unfortunately, we also discover
that the heartbeat interval signal captured by piezo vibration sensors has low
Signal-to-Noise Ratio (SNR) because they are not designed as precision
heartbeat monitors, which becomes the key challenge for H2B. To overcome this
problem, we first apply a quantile function-based quantization method to fully
extract the useful entropy from the noisy piezo measurements. We then propose a
novel Compressive Sensing-based reconciliation method to correct the high bit
mismatch rates between the two independently generated keys caused by low SNR.
We prototype H2B using off-the-shelf piezo sensors and evaluate its performance
on a dataset collected from different body positions of 23 participants. Our
results show that H2B has an overwhelming pairing success rate of 95.6%. We
also analyze and demonstrate H2B's robustness against three types of attacks.
Finally, our power measurements show that H2B is very power-efficient
Biometric authentication using the PPG: A long-term feasibility study
The photoplethysmogram (PPG) is a biomedical signal that can be used to estimate volumetric blood flow changes in the peripheral circulation. During the past few years, several works have been published in order to assess the potential for PPGs to be used in biometric authentication systems, but results are inconclusive. In this paper we perform an analysis of the feasibility of using the PPG as a realistic biometric alternative in the long term. Several feature extractors (based on the time domain and the Karhunen–Loève transform) and matching metrics (Manhattan and Euclidean distances) have been tested using four different PPG databases (PRRB, MIMIC-II, Berry, and Nonin). We show that the false match rate (FMR) and false non-match rate (FNMR) values remain constant in different time instances for a selected threshold, which is essential for using the PPG for biometric authentication purposes. On the other hand, obtained equal error rate (EER) values for signals recorded during the same session range from 1.0% for high-quality signals recorded in controlled conditions to 8% for those recorded in conditions closer to real-world scenarios. Moreover, in certain scenarios, EER values rise up to 23.2% for signals recorded over different days, signaling that performance degradation could take place with time
Transcending conventional biometry frontiers: Diffusive Dynamics PPG Biometry
In the first half of the 20th century, a first pulse oximeter was available
to measure blood flow changes in the peripheral vascular net. However, it was
not until recent times the PhotoPlethysmoGraphic (PPG) signal used to monitor
many physiological parameters in clinical environments. Over the last decade,
its use has extended to the area of biometrics, with different methods that
allow the extraction of characteristic features of each individual from the PPG
signal morphology, highly varying with time and the physical states of the
subject. In this paper, we present a novel PPG-based biometric authentication
system based on convolutional neural networks. Contrary to previous approaches,
our method extracts the PPG signal's biometric characteristics from its
diffusive dynamics, characterized by geometric patterns image in the (p,
q)-planes specific to the 0-1 test. The diffusive dynamics of the PPG signal
are strongly dependent on the vascular bed's biostructure, which is unique to
each individual, and highly stable over time and other psychosomatic
conditions. Besides its robustness, our biometric method is anti-spoofing,
given the convoluted nature of the blood network. Our biometric authentication
system reaches very low Equal Error Rates (ERRs) with a single attempt, making
it possible, by the very nature of the envisaged solution, to implement it in
miniature components easily integrated into wearable biometric systems.Comment: 18 pages, 6 figures, 4 table
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A Survey of Wearable Biometric Recognition Systems
The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner’s physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work
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