# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 1 Introduction  
In an era characterized by ubiquitous digital interactions, the need for robust and secure human identification systems has become paramount. Traditional recognition methods like personal identification numbers (PINs) [1] and passwords have become vulnerable to attacks and can be lost or forgotten. As a result, there is a need to develop a biometric system that is more secure. PINs are increasingly protected using biometrics to address the threat of loss or theft. These technologies safely manage personal information and verify the user's identity. Biometric recognition is an approach that provides a unique methodfor identity recognition. It uses metrics related to human characteristics for recognition. Biometrics has many features, such as uniqueness, permanance, and ease of collection. It involves both behavioral and physical characteristics. Behavioral factors include voice, gait, and electrocardiogram (ECG), while Physical features include the face, fingerprints, and iris [2]. However, traditional biometrics needs help with many issues, like addressing spoofing or forgery. As a result, there has been a growing interest in leveraging biometric authentication methods, which rely on individuals' unique physiological and behavioral characteristics, to enhance security and reduce vulnerabilities. Cardiac biometrics have emerged as a promising and innovative approach for human identification among the myriad biometric modalities. The human heart's intrinsic electrical activity, captured using electrocardiogram (ECG), photoplethysmograph (PPG), and phonocardiogram (PCG) signals [3], offers a wealth of valuable information that can be leveraged for secure authentication. Utilizing electrocardiogram (ECG) signals for biometric-based human identification provides several distinct advantages over alternative approaches like photoplethysmograph (PPG) and phonocardiogram (PCG) signals. ECG signals offer a unique and highly individualistic biometric marker [4] due to the heart's intricate electrical activity patterns. This uniqueness enhances the security and accuracy of identification systems. Unlike PPG, which primarily measures blood volume changes and can be influenced by external factors, ECG signals directly reflect the cardiac electrical patterns, making them more robust and reliable in varied conditions. Additionally, ECG signals are less susceptible to mimicry or spoofing than PPG and PCG signals, which can be more easily replicated using external devices. The stability and consistency of ECG features across different states (e.g., resting and exercising) further strengthen its suitability for biometric applications. Lastly, the non-intrusive nature of ECG measurements, usually captured using electrodes placed on the skin, ensures user comfort and encourages wider adoption. These combined advantages position ECG signals as superior for accurate, secure, and reliable biometric-based human identification systems, setting them apart from PPG and PCG approaches. In this context, the present paper delves into developing a novel cardiac biometrics system, fortified by deep learning (DL) [5] approaches, to achieve secure human identification. Deep learning has exhibited remarkable capabilities in handling complex patterns and representations in diverse data domains, and its application to cardiac biometrics presents an exciting frontier in the pursuit of reliable and efficient authentication systems. The central aim of this paper is to propose an ensemble approach that effectively harnesses the strengths of pre-trained VGG16 transfer learning (TL) and Long Short-Term Memory (LSTM) architectures [6] for optimal feature extraction from 2D spectrogram images derived from ECG biosignals. The ensemble feature representation capitalizes on the spatial and temporal characteristics of the cardiac signals, providing a holistic and discriminative basis for human identification.