Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
-
Published:2023-08-21
Issue:16
Volume:13
Page:9454
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Aleidan Anfal Ahmed1, Abbas Qaisar1ORCID, Daadaa Yassine1, Qureshi Imran1, Perumal Ganeshkumar1, Ibrahim Mostafa E. A.12ORCID, Ahmed Alaa E. S.13
Affiliation:
1. College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia 2. Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Benha 13518, Qalubia, Egypt 3. Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt
Abstract
User authentication has become necessary in different life domains. Traditional authentication methods like personal information numbers (PINs), password ID cards, and tokens are vulnerable to attacks. For secure authentication, methods like biometrics have been developed in the past. Biometric information is hard to lose, forget, duplicate, or share because it is a part of the human body. Many authentication methods focused on electrocardiogram (ECG) signals have achieved great success. In this paper, we have developed cardiac biometrics for human identification using a deep learning (DL) approach. Cardiac biometric systems rely on cardiac signals that are captured using the electrocardiogram (ECG), photoplethysmogram (PPG), and phonocardiogram (PCG). This study utilizes the ECG as a biometric modality because ECG signals are a superior choice for accurate, secure, and reliable biometric-based human identification systems, setting them apart from PPG and PCG approaches. To get better performance in terms of accuracy and computational time, we have developed an ensemble approach based on VGG16 pre-trained transfer learning (TL) and Long Short-Term Memory (LSTM) architectures to optimize features. To develop this authentication system, we have fine-tuned this ensemble network. In the first phase, we preprocessed the ECG biosignal to remove noise. In the second phase, we converted the 1-D ECG signals into a 2-D spectrogram image using a transformation phase. Next, the feature extraction step is performed on spectrogram images using the proposed ensemble DL technique, and finally, those features are identified by the boosting machine learning classifier to recognize humans. Several experiments were performed on the selected dataset, and on average, the proposed system achieved 98.7% accuracy, 98.01% precision, 97.1% recall, and 0.98 AUC. In this paper, we have compared the developed approach with state-of-the-art biometric authentication systems. The experimental results demonstrate that our proposed system outperformed the human recognition competition.
Funder
Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference62 articles.
1. Wang, D., Si, Y., Yang, W., Zhang, G., and Li, J. (2019). A Novel Electrocardiogram Biometric Identification Method Based on Temporal-Frequency Autoencoding. Electronics, 8. 2. Lee, J.-A., and Kwak, K.-C. (2022). Personal Identification Using an Ensemble Approach of 1D-LSTM and 2D-CNN with Electrocardiogram Signals. Appl. Sci., 12. 3. A Survey on Heart Biometrics;Rathore;ACM Comput. Surv.,2020 4. An Intelligent Approach for Person Identification Using Phonocardiogram Signals;Bassiouni;Int. J. Appl. Fuzzy Sets Artif. Intell.,2016 5. Deep learning;Yann;Nature,2015
|
|