# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 6 Conclusions  
In this paper, we have developed a novel cardiac biometric system for human identification using deep learning approaches. The system leverages the unique physiological characteristics of individuals captured using electrocardiogram (ECG), photoplethysmogram (PPG), and phonocardiogram (PCG) signals. To achieve secure authentication, we proposed an ensemble approach based on pre-trained VGG16 transfer learning (TL) and Long Short-Term Memory (LSTM) architectures.  
The system follows a multi-phase approach. In the first phase, we preprocessed the ECG biosignals to remove noise and ensure data quality. The second phase involved converting the 1-D ECG signals into 2-D spectrogram images and capturing temporal and frequency information. Feature extraction was performed in the third phase using the ensemble DL technique, combining VGG16 and LSTM models to obtain intermediate feature representations. The extracted ensemble features were then utilized as input for a boosting machine learning classifier, enabling the system to recognize and identify individuals based on their cardiac biometric data. Extensive experiments were conducted on a selected dataset to evaluate the system's performance. On average, our proposed approach achieved an accuracy of 0.98%, a sensitivity of 0.98%, a specificity of 0.96%, and an AUC of 0.95. In comparison to state-of-the-art biometric authentication systems, our developed approach demonstrated superior performance in recognizing humans based on their cardiac biometric information. The combination of spatial features from the VGG16 model and temporal patterns from the LSTM model allowed us to optimize the ensemble features, leading to enhanced accuracy and robustness in the identification process. The proposed cardiac biometrics system holds significant potential for various real-world applications, particularly in domains requiring secure authentication. Its utilization in healthcare, finance, access control, and other sensitive areas could provide reliable and efficient human identification. Future research can focus on further refining the system, exploring additional deep learning architectures, and testing the approach on larger and more diverse datasets to strengthen its practicality and generalizability.  
Conceptualization, A.A.A., Q.A., Y.D., I.Q., G.P., M.E.A.I. and A.E.S.A.; Data curation, A.A.A., Y.D., M.E.A.I. and A.E.S.A.; Formal analysis, A.A.A., Y.D., I.Q., G.P., M.E.A.I. and A.E.S.A.; Funding acquisition, Q.A. and A.E.S.A.; Investigation, Y.D., G.P., M.E.A.I. and A.E.S.A.; Methodology, Q.A. and Y.D.; Project administration, Q.A. and I.Q.; Resources, Y.D., I.Q., G.P. and A.E.S.A.; Software, A.A.A., Y.D., G.P., M.E.A.I. and A.E.S.A.; Supervision, Q.A. and G.P.; Validation, Q.A., Y.D., I.Q. and M.E.A.I.; Visualization, A.A.A., I.Q. and M.E.A.I.; Writing\(-\)original draft, A.A.A., Q.A., Y.D., I.Q., G.P., M.E.A.I. and A.E.S.A.; Writing\(-\)review and editing, I.Q., G.P., M.E.A.I. and A.E.S.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU- RG23082).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
**Acknowledgments:** This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU- RG23082).  
**Conflicts of Interest:** The authors declare no conflict of interest.