Biometric personal identification system using the ECG signal (original) (raw)

Personal identity verification based ECG biometric using non-fiducial features

International Journal of Electrical and Computer Engineering (IJECE), 2020

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.

Biometric Identification System Based on Electrocardiogram Data

2008

Recent advancements in computing and digital signal processing technologies have made automated identification of people based on their biological, physiological, or behavioral traits a feasible approach for access control. The wide variety of available technologies has also increased the number of traits and features that can be collected and used to more accurately identify people. Systems that use biological, physiological, or behavioral trait to grant access to resources are called biometric systems. In this paper we present a biometric identification system based on the Electrocardiogram (ECG) signal. The system extracts 24 temporal and amplitude features from an ECG signal and after processing, reduces the set of features to the nine most relevant features. Preliminary experimental results indicate that the system is accurate and robust and can achieve a 100% identification rate with the reduced set of features.

Analysis of Human Electrocardiogram for Biometric Recognition

EURASIP Journal on Advances in Signal Processing, 2007

Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric trait, the human heartbeat, can be used for identity recognition. Existing solutions for biometric recognition from electrocardiogram (ECG) signals are based on temporal and amplitude distances between detected fiducial points. Such methods rely heavily on the accuracy of fiducial detection, which is still an open problem due to the difficulty in exact localization of wave boundaries. This paper presents a systematic analysis for human identification from ECG data. A fiducial-detection-based framework that incorporates analytic and appearance attributes is first introduced. The appearance-based approach needs detection of one fiducial point only. Further, to completely relax the detection of fiducial points, a new approach based on autocorrelation (AC) in conjunction with discrete cosine transform (DCT) is proposed. Experimentation demonstrates that the AC/DCT method produces comparable recognition accuracy with the fiducial-detection-based approach.

A Novel Biometric Based on ECG Signals and Images for Human Authentication

This paper represents a complete system for using Electrocardiogram (ECG) images for human authentication. In this study, the proposed algorithm is divided into three main stages: Pre-processing stage, feature extraction stage and classification stage. A real database is used; it consists of 120 ECG images which are collected from 20 persons. The preprocessing stage is done on the ECG image. Preprocessing should remove all variations and details from an ECG image that are meaningless to the authentication method. In addition, this paper discusses briefly an extended version of work previously published on ECG feature extraction. In classification stage, Neural Network is used to make persons authentication. At the end, a system for real-time authentication is built. The proposed system achieves high sensitivity results for extracting ECG features and for human authentication.

Biometrics Authentication using Electrocardiogram Approach

Engineering and Scientific International Journal (ESIJ) , 2018

Biometrics is a secure alternative for traditional methods in identity verification [1]. Electrocardiogram (ECG) is an emerging biometric security mechanism. Biometric measures are used in many different areas and industries to provide a relatively high level security. The word biological is based on DeoxyriboNucleic Acid (DNA), behavioural is based on gait or keystroke dynamic, and morphological is based on uniqueness for all people like fingerprint or face etc. [2]. ECG is combined with commonly used face biometric and fingerprint biometric. The uniqueness of the electrocardiogram signal has encouraged its use in building different biometric identification systems. It is also a source of supplementary information to a multi biometric system; it shows moderate performance in a uni-model framework. The concerns involved to use ECG as a biometric for individual authentication are the lack of standardization in signal features and the presence of acquisition variations. Otherwise this make the errant users to hack the data or information.

ECG Biometric Authentication: A Comparative Analysis

IEEE Access

Robust authentication and identification methods become an indispensable urgent task to protect the integrity of the devices and the sensitive data. Passwords have provided access control and authentication, but have shown their inherent vulnerabilities. The speed and convenience factor are what makes biometrics the ideal authentication solution as they could have a low probability of circumvention. To overcome the limitations of the traditional biometric systems, electrocardiogram (ECG) has received the most attention from the biometrics community due to the highly individualized nature of the ECG signals and the fact that they are ubiquitous and difficult to counterfeit. However, one of the main challenges in ECG-based biometric development is the lack of large ECG databases. In this paper, we contribute to creating a new large gallery off-the-person ECG datasets that can provide new opportunities for the ECG biometric research community. We explore the impact of filtering type, segmentation, feature extraction, and health status on ECG biometric by using the evaluation metrics. Our results have shown that our ECG biometric authentication outperforms existing methods lacking the ability to efficiently extract features, filtering, segmentation, and matching. This is evident by obtaining 100% accuracy for PTB, MIT-BHI, CEBSDB, CYBHI, ECG-ID, and in-house ECG-BG database in spite of noisy, unhealthy ECG signals while performing five-fold cross-validation. In addition, an average of 2.11% EER among 1,694 subjects is obtained.

Utilizing ECG Waveform Features as New Biometric Authentication Method

International Journal of Electrical and Computer Engineering (IJECE), 2018

In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects" raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.

Cancelable biometric authentication system based on ECG

Biometrics are widely deployed in various security systems; however, they have drawbacks in the form of leakage or stealing, therefore numerous solutions have been proposed to secure biometric template such as cancelable biometric, which is one of the possible solutions for canceling and securing biometric template. However, this problem is still open and to the best of our knowledge, few previous studies have proposed a complete authentic system using the cancelable biometric techniques based on electrocardiogram (ECG). In this paper, we have applied two cancelable biometric techniques for developing a human authentication system based on ECG signals. The first one is an improved Bio-Hashing and the second one is matrix operation technique. The improved Bio-Hash technique solves the problem of accuracy loss, which is the main drawback of basic Bio-Hash technique. The protected feature vector (Bio-Hashed code) is generated from the inner product between the ECG features matrix and tokenize number matrix. While the matrix operation technique is applied on the ECG feature matrix to produce a transformed template which is irreversible to the original features of the ECG. In the authentication stage, Feed-Forward Neural Network (FFNN) is used to verify individuals. After applying the two cancelable techniques on three public available ECG databases, experimental results show that the proposed system performs better regarding authentication and outperforms state-ofthe-art techniques considered.

A novel biometric authentication approach using electrocardiogram signals

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2013

In this work, we present a novel biometric authentication approach based on combination of AC/DCT features, MFCC features, and QRS beat information of the ECG signals. The proposed approach is tested on a subset of 30 subjects selected from the PTB database. This subset consists of 13 healthy and 17 non-healthy subjects who have two ECG records. The proposed biometric authentication approach achieves average frame recognition rate of %97.31 on the selected subset. Our experimental results imply that the frame recognition rate of the proposed authentication approach is better than that of ACDCT and MFCC based biometric authentication systems, individually.

Evaluation of Electrocardiogram for Biometric Authentication

Journal of Information Security, 2012

This paper presents an evaluation of a new biometric electrocardiogram (ECG) for individual authentication. We report the potential of ECG as a biometric and address the research concerns to use ECG-enabled biometric authentication system across a range of conditions. We present a method to delineate ECG waveforms and their end fiducials from each heartbeat. A new authentication strategy is proposed in this work, which uses the delineated features and taking decision for the identity of an individual with respect to the template database on the basis of match scores. Performance of the system is evaluated in a unimodal framework and in the multibiometric framework where ECG is combined with the face biometric and with the fingerprint biometric. The equal error rate (EER) result of the unimodal system is reported to 10.8%, while the EER results of the multibiometric systems are reported to 3.02% and 1.52%, respectively for the systems when ECG combined with the face biometric and ECG combined with the fingerprint biometric. The EER results of the combined systems prove that the ECG has an excellent source of supplementary information to a multibiometric system, despite it shows moderate performance in a unimodal framework. We critically evaluate the concerns involved to use ECG as a biometric for individual authentication such as, the lack of standardization of signal features and the presence of acquisition variations that make the data representation more difficult. In order to determine large scale performance, individuality of ECG remains to be examined.