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.

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.

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.