Personal identity verification based ECG biometric using non-fiducial features

Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision

Implementation of Kernel Sparse Representation Classifier for ECG Biometric System

In this paper, a biometric human recognition system based on Electrocardiography (ECG) signal is proposed. Three processes i.e., pre-processing, feature extraction and classification is discussed. A combination of enhanced start and end point detection namely short time energy (STE) and short time average zero crossing rate (STAZCR) is employed in the pre-processing. Subsequently, an autocorrelation method is applied in feature extraction. For the classification process, the kernel sparse representation classifier (KSRC) is proposed as a classifier to increase the system performance in high dimensional feature space. 79 recorded signals from 79 subjects are used are employed in this study. To validate the performance of the KSRC, several classifiers, i.e. sparse representation classifier (SRC), k nearest neighbor (kNN) and support vector machine (SVM) are compared. An experiment based on different sizes of feature dimensions is conducted. The classification performance for four classifiers are found to be 90.93%, 92.8%, 94.24%, 62.9%, 97.23% and 95.87% for the kNN, SVM (Polynomial and RBF), SRC and KSRC (Polynomial and RBF), respectively. The results reveal that the KSRC is a promising classifier for the ECG biometric system compared to the existing reference classifiers

Unveiling the Biometric Potential of Finger-Based ECG Signals

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications

Wavelets in ECG Security Application

Wavelet packet transform has been used in many applications of biomedical signal processing, for example, feature extraction, noise reduction, data compression, electrocardiogram (ECG) anonymisation and QRS detection. The wavelet analysis methods, in these applications, represent the temporal characteristics of a biological signal by its spectral components in the frequency domain. Furthermore, it has been shown in many works that the ECG signal can be used as a biometric method for robust human identification and authentication. In this case, it is necessary to anonymise the ECG data during the distribution and storage of the signal in a public repository. A neglectful system leads to an eavesdropper recording the ECG data and uses it as recognition data to gain access via an ECG biometric system. This chapter discusses and reviews recent researches on ECG anonymisation wavelets-based techniques. These techniques use discrete wavelet transform and wavelet packet transform. A comparative study between the wavelets-based methods will be presented

Identifying Humans by the Shape of Their Heartbeats and Materials by Their X-Ray Scattering Profiles

Security needs at access control points presents itself in the form of human identification and/or material identification. The field of Biometrics deals with the problem of identifying individuals based on the signal measured from them. One approach to material identification involves matching their x-ray scattering profiles with a database of known materials. Classical biometric traits such as fingerprints, facial images, speech, iris and retinal scans are plagued by potential circumvention they could be copied and later used by an impostor. To address this problem, other bodily traits such as the electrical signal acquired from the brain (electroencephalogram) or the heart (electrocardiogram) and the mechanical signals acquired from the heart (heart sound, laser Doppler vibrometry measures of the carotid pulse) have been investigated. These signals depend on the physiology of the body, and require the individual to be alive and present during acquisition, potentially overcoming circumvention. We investigate the use of the electrocardiogram (ECG) and carotid laser Doppler vibrometry (LDV) signal, both individually and in unison, for biometric identity recognition. A parametric modeling approach to system design is employed, where the system parameters are estimated from training data. The estimated model is then validated using testing data. A typical identity recognition system can operate in either the authentication (verification) or identification mode. The performance of the biometric identity recognition systems is evaluated using receiver operating characteristic (ROC) or detection error tradeoff (DET) curves, in the authentication mode, and cumulative match characteristic (CMC) curves, in the identification mode. The performance of the ECG- and LDV-based identity recognition systems is comparable, but is worse than those of classical biometric systems. Authentication performance below 1% equal error rate (EER) can be attained when the training and testing data are obtained from a single measurement session. When the training and testing data are obtained from different measurement sessions, allowing for a potential short-term or long-term change in the physiology, the authentication EER performance degrades to about 6 to 7%. Leveraging both the electrical (ECG) and mechanical (LDV) aspects of the heart, we obtain a performance gain of over 50%, relative to each individual ECG-based or LDV-based identity recognition system, bringing us closer to the performance of classical biometrics, with the added advantage of anti-circumvention. We consider the problem of designing combined x-ray attenuation and scatter systems and the algorithms to reconstruct images from the systems. As is the case within a computational imaging framework, we tackle the problem by taking a joint system and algorithm design approach. Accurate modeling of the attenuation of incident and scattered photons within a scatter imaging setup will ultimately lead to more accurate estimates of the scatter densities of an illuminated object. Such scattering densities can then be used in material classification. In x-ray scatter imaging, tomographic measurements of the forward scatter distribution are used to infer scatter densities within a volume. A mask placed between the object and the detector array provides information about scatter angles. An efficient computational implementation of the forward and backward model facilitates iterative algorithms based upon a Poisson log-likelihood. The design of the scatter imaging system influences the algorithmic choices we make. In turn, the need for efficient algorithms guides the system design. We begin by analyzing an x-ray scatter system fitted with a fanbeam source distribution and flat-panel energy-integrating detectors. Efficient algorithms for reconstructing object scatter densities from scatter measurements made on this system are developed. Building on the fanbeam source, energy-integrating at-panel detection model, we develop a pencil beam model and an energy-sensitive detection model. The scatter forward models and reconstruction algorithms are validated on simulated, Monte Carlo, and real data. We describe a prototype x-ray attenuation scanner, co-registered with the scatter system, which was built to provide complementary attenuation information to the scatter reconstruction and present results of applying alternating minimization reconstruction algorithms on measurements from the scanner

Pulse Active Transform (PAT): A non-invertible transformation with application to ECG biometric authentication

This paper presents a new transformation technique called the Pulse Active transform (PAT). The PAT uses a series of harmonically related periodic triangular waveforms to decompose a signal into a finite set of pulse active features. These features incorporate the signal's information in the pulse active domain, and which are subsequently processed for some desired application. PAT is non-invertible thus ensuring complete security of the original signal source. In this paper PAT is demonstrated on an ECG signal and used for biometric authentication. The new transformation technique is tested on 112 PTB subjects. It is shown in this paper that the new transformation has a superior performance compared to the conventional characteristic based feature extraction methods with additional security to avoid recovery of the original ECG

Assentication: User Deauthentication and Lunchtime Attack Mitigation with Seated Posture Biometric

Biometric techniques are often used as an extra security factor in authenticating human users. Numerous biometrics have been proposed and evaluated, each with its own set of benefits and pitfalls. Static biometrics (such as fingerprints) are geared for discrete operation, to identify users, which typically involves some user burden. Meanwhile, behavioral biometrics (such as keystroke dynamics) are well suited for continuous, and sometimes more unobtrusive, operation. One important application domain for biometrics is deauthentication, a means of quickly detecting absence of a previously authenticated user and immediately terminating that user's active secure sessions. Deauthentication is crucial for mitigating so called Lunchtime Attacks, whereby an insider adversary takes over (before any inactivity timeout kicks in) authenticated state of a careless user who walks away from her computer. Motivated primarily by the need for an unobtrusive and continuous biometric to support effective deauthentication, we introduce PoPa, a new hybrid biometric based on a human user's seated posture pattern. PoPa captures a unique combination of physiological and behavioral traits. We describe a low cost fully functioning prototype that involves an office chair instrumented with 16 tiny pressure sensors. We also explore (via user experiments) how PoPa can be used in a typical workplace to provide continuous authentication (and deauthentication) of users. We experimentally assess viability of PoPa in terms of uniqueness by collecting and evaluating posture patterns of a cohort of users. Results show that PoPa exhibits very low false positive, and even lower false negative, rates. In particular, users can be identified with, on average, 91.0% accuracy. Finally, we compare pros and cons of PoPa with those of several prominent biometric based deauthentication techniques

Human Identification Based on Electrocardiogram and Palmprint

In this paper, a new approach in human identification is investigated. For this purpose, we fused ECG and Palm print biometrics to achieve a multimodal biometric system. In the proposed system for fusing biometrics, we used MFCC approach in order to extract features of ECG biometric and PCA to extract features of Palm print. The features undergo a KNN classification. The performance of the algorithm is evaluated against the standard MIT-BIH and POLYU databases. Moreover, in order to achieve more realistic and reliable results, we gathered Holter ECG recordings acquired from 50 male and female subjects in age between 18 and 54. The numerical results indicated that the algorithm achieved 94.7% of the detection rate.DOI:http://dx.doi.org/10.11591/ijece.v2i2.29

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