# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 1 Introduction
### Background  
Biometric authentication, which relies on individuals' unique physiological and behavioral characteristics for identification, has gained significant attention due to its potential to enhance security and user convenience in various applications. Cardiac biometrics presents an intriguing and promising avenue for secure human identification among the diverse biometric modalities. Traditional authentication methods, such as passwords and PINs, are susceptible to various attacks, leading to data breaches and identity theft. As a result, researchers and industries alike have turned to biometrics as a more robust and user-friendly alternative. Biometric modalities like fingerprint, iris, and facial recognition have widespread adoption but may suffer from environmental constraints, usability challenges, and potential privacy concerns. In contrast, cardiac biometrics harness the unique physiological characteristics of the human heart, making them inherently difficult to replicate, forget, or lose. The Electrocardiogram (ECG), photoplethysmograph (PPG), and phonocardiogram (PCG) are some of the cardiac signals that hold valuable information about an individual's cardiac rhythm, heart rate, and other distinctive features. The rise of deep learning has further accelerated advancements in biometric authentication systems. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) [6], have shown remarkable capabilities in handling complex patterns and representations in various data domains, including images, sequences, and time-series data. Building on this context, the present research endeavors to develop a novel cardiac biometric system for human identification using deep learning approaches. By combining the strengths of deep learning with the unique characteristics of cardiac signals, the goal is to achieve accurate and secure human identification, surpassing the limitations of traditional authentication methods. The proposed research addresses critical challenges in cardiac biometric authentication, such as noise reduction, feature extraction, and classification. The use of an ensemble approach, combining pre-trained VGG16 transfer learning (TL) and Long Short-Term Memory (LSTM) architectures, is expected to offer a comprehensive and discriminative representation of cardiac features, capturing both spatial and temporal aspects of the signals. Electrocardiogram (ECG) signals offer a distinct advantage over photoplethysmograph (PPG) and electroencephalogram (EEG) signals [7] due to their precision in assessing cardiac activity, which is unique for individuals. ECG provides a detailed and highly accurate representation of the heart's electrical activity, enabling the detection of patterns. In contrast with PPG and EEG signals, the ECG's ability to offer comprehensive insights into cardiac health makes it a superior choice for detailed cardiovascular assessments [8]. Therefore, we have used ECG signals compared to PPG and EEG signals for human detection. The ECG signal is a biometric heart signal. Recent research has focused on using ECG signals for biometric identification due to their characteristics. In practice, it is more difficult to commit fraud. In the past, many studies used machine learning (ML) or deep learning (DL) techniques for identifying a person using heart signals. Compared to ML-based systems, DL-based methods outperformed heart biometrics. Those studies utilized ECG signals for human identification by developing a deep-learning model. However, those studies are not up-to-date because the ECG signals contain many noises or artifacts, making it challenging to recognize humans. Those methods are computationally expensive for fine-tuning the network and selecting hyperparameters. In addition, it is not easy to represent the signals in time and frequency domains to identify humans based on ECG signals. Figure 1 shows A visual example of biometric-based human identification using ECG signal processing and a machine learning algorithm.