# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 5 Discussions  
The demand for robust and secure human identification systems has reached a critical juncture in today's digital landscape, characterized by widespread and continuous digital interactions. Conventional recognition methods, such as personal identification numbers (PINs) and passwords, have become increasingly susceptible to attack, resulting in potential security breaches, lost credentials, or forgotten access codes. As a result, there is a compelling need to innovate and develop biometric systems that offer heightened security and reliability. Recognizing this trend, modern practices have seen a rise in the use of biometric methods to augment or replace traditional PIN-based security measures, mitigating the vulnerabilities posed by the loss or theft of personal identification information. These \begin{table}
\begin{tabular}{c c c} \hline \hline
**Step** & **Complexity** & **Example Size (n)** \\ \hline Preprocessing Steps & O (n\({}^{2}\)) & 1000 \\
1D ECG to 2D Spectrogram Transformation & O (n) (approx.) & 1000 \\ Feature Extraction (VGG16 Pretrained Model) & O (1,611,840) & 224 \\ Feature Optimization (LSTM) & O (838,860,800) & 256 \\ Biometric Identification (LightGBM Classifier) & O (3,845,620) & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Computational analysis of a proposed system to identify humans. Figure 12: Different machine learning and deep learning results were obtained by the proposed system (VGG16-LSTM and LightGBM) compared to other standard techniques. advanced biometric technologies securely manage personal information and verify users' identities with accuracy and assurance unmatched by conventional methods. Biometric recognition encompasses a diverse range of physiological and behavioral human characteristics, including attributes like voice, gait, and electrocardiogram (ECG) signals and physical traits such as facial features, fingerprints, and iris patterns. However, conventional biometric systems face challenges, particularly regarding spoofing and forgery. Consequently, there has been a growing interest in adopting biometric authentication methods that harness an individual's unique physiological and behavioral traits to enhance security and alleviate vulnerabilities significantly. Cardiac biometrics have emerged as a promising and innovative approach to address the imperative of human identification security among the diverse biometric modalities. At the core of cardiac biometrics lies the intrinsic electrical activity of the human heart, encapsulated in signals such as the electrocardiogram (ECG), photoplethysmograph (PPG), and phonocardiogram (PCG). These signals carry rich and distinctive information that can be harnessed for secure authentication. Within this landscape, the utilization of ECG signals for biometric-based human identification stands out due to its unique advantages over alternative approaches like PPG and PCG signals. ECG signals provide an inherently individualistic biometric marker owing to the intricate patterns woven into the heart's electrical activity. This inherent uniqueness significantly bolsters the security and precision of identification systems. These advantages position ECG signals as an optimal choice for constructing biometric-based human identification systems distinguished by accuracy, safety, and reliability, setting them apart from PPG and PCG approaches. Deep learning has demonstrated exceptional prowess in deciphering complex patterns and representations across diverse data domains, making its integration into cardiac biometrics an exciting frontier in the pursuit of dependable and efficient authentication systems. The primary objective of this study is to introduce an ensemble methodology that effectively harnesses the strengths of a pre-trained VGG16 transfer learning (TL) framework in tandem with long-short-term memory (LSTM) architectures. This amalgamation aims to extract optimal features from two-dimensional spectrogram images derived from ECG biosignals.