169 research outputs found
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
Identifikasi Personal Biometrik Berdasarkan Sinyal Photoplethysmography dari Detak Jantung
Sistem biometrik sangat berguna untuk membedakan karakteristik individu
seseorang. Sistem identifikasi yang paling banyak digunakan diantaranya berdasarkan
metode fingerprint, face detection, iris atu hand geometry. Penelitian ini mencoba
untuk meningkatkan sistem biometrik menggunakan sinyal Photoplethysmography
dari detak jantung. Algoritma yang diusulkan menggunakan seluruh ektraksi fitur
yang didapatkan melalui sistem untuk pengenalan biometrik. Efesiensi dari algoritma
yang diusulkan didemonstrasikan oleh hasil percobaan yang didapatkan menggunakan
metode klasifikasi Multilayer Perceptron, Naïve Bayes dan Random Forest
berdasarkan fitur ekstraksi yang didapatkan dari proses sinyal prosesing. Didapatkan
51 subjek pada penelitian ini; sinyal PPG signals dari setiap individu didapatkan
melalui sensor pada dua rentang waktu yang berbeda. 30 fitur karakteristik didapatkan
dari setiap periode dan kemudian digunakan untuk proses klasifikasi. Sistem
klasifikasi menggunakan metode Multilayer Perceptron, Naïve Bayes dan Random
Forest; nilai true positive dari masing-masing metode adalah 94.6078 %, 92.1569 %
dan 90.3922 %. Hasil yang didapatkan menunjukkan bahwa seluruh algoritma yang
diusulkan dan sistem identifikasi biometrik dari pengembangan sinyal PPG ini sangat menjanjikan untuk sistem pengenalan individu manusia.
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The importance of biometric system can distinguish the uniqueness of personal
characteristics. The most popular identification systems have concerned the method
based on fingerprint, face detection, iris or hand geometry. This study is trying to
improve the biometric system using Photoplethysmography signal by heart rate. The
proposed algorithm calculates the contribution of all extracted features to biometric
recognition. The efficiency of the proposed algorithms is demonstrated by the
experiment results obtained from the Multilayer Perceptron, Naïve Bayes and
Random Forest classifier applications based on the extracted features. There are fifty
one persons joined for the experiments; the PPG signals of each person were recorded
for two different time spans. 30 characteristic features were extracted for each period
and these characteristic features are used for the purpose of classification. The results
were evaluated via the Multilayer Perceptron, Naïve Bayes and Random Forest
classifier models; the true positive rates are then 94.6078 %, 92.1569 % and 90.3922
%, respectively. The obtained results showed that both the proposed algorithm and the
biometric identification model based on this developed PPG signal are very promising
for contact less recognizing systems
Seeing Red: PPG Biometrics Using Smartphone Cameras
In this paper, we propose a system that enables photoplethysmogram
(PPG)-based authentication by using a smartphone camera. PPG signals are
obtained by recording a video from the camera as users are resting their finger
on top of the camera lens. The signals can be extracted based on subtle changes
in the video that are due to changes in the light reflection properties of the
skin as the blood flows through the finger. We collect a dataset of PPG
measurements from a set of 15 users over the course of 6-11 sessions per user
using an iPhone X for the measurements. We design an authentication pipeline
that leverages the uniqueness of each individual's cardiovascular system,
identifying a set of distinctive features from each heartbeat. We conduct a set
of experiments to evaluate the recognition performance of the PPG biometric
trait, including cross-session scenarios which have been disregarded in
previous work. We found that when aggregating sufficient samples for the
decision we achieve an EER as low as 8%, but that the performance greatly
decreases in the cross-session scenario, with an average EER of 20%.Comment: 8 pages, 15th IEEE Computer Society Workshop on Biometrics 202
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
Photoplethysmogram based biometric identification for twins incorporating gender variability
This study focuses on a Photoplethysmogram (PPG) based biometric identification for twins incorporating gender variability. To the best of our knowledge, little has been said pertaining to this research which identifies twins using PPG signals. PPG device has been widely used due to its advantages such as non-invasive, low cost and small in size which makes it a convenient analytical tool. PPG signals has the capability to ensure the person to be present during the acquisition process which suggest that PPG can provide liveness detection suitable for a biometric system which is not available in other biometric modalities such as fingerprint. A total of four couple of twins which consists of four female and four male subjects in age range between twenty two to thirty years old were used to assess the feasibility of the proposed system. The acquired PPG signals were then processed to remove unwanted noise using low pass filter. After that, multiple cycles of PPG waveforms were extracted and later classified using Radial Basis Function (RBF) and Bayes Network (BN) to categorize the subjects using the discriminant features to calculate and analyze the performance of this system. The outcome also provides a complimentary mechanism to detect twins besides using the current existing methods
Shallow Neural Network for Biometrics from the ECG-WATCH
Applications such as surveillance, banking and healthcare deal with sensitive data whose confidentiality and integrity depends on accurate human recognition. In this sense, the crucial mechanism for performing an effective access control is authentication, which unequivocally yields user identity. In 2018, just in North America, around 445K identity thefts have been denounced. The most adopted strategy for automatic identity recognition uses a secret for encrypting and decrypting the authentication information. This approach works very well until the secret is kept safe. Electrocardiograms (ECGs) can be exploited for biometric purposes because both the physiological and geometrical differences in each human heart correspond to uniqueness in the ECG morphology. Compared with classical biometric techniques, e.g. fingerprints, ECG-based methods can definitely be considered a more reliable and safer way for user authentication due to ECG inherent robustness to circumvention, obfuscation and replay attacks. In this paper, the ECG WATCH, a non-expensive wristwatch for recording ECGs anytime, anywhere, in just 10 s, is proposed for user authentication. The ECG WATCH acquisitions have been used to train a shallow neural network, which has reached a 99% classification accuracy and 100% intruder recognition rate
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
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