This study presents an exploratory framework for electrocardiogram (ECG)-based biometric identification using ECG signals acquired using biocompatible, solid-state conductive electrodes. The system addresses key challenges in ECG biometrics, including user convenience, temporal stability, and minimal data requirements. ECG signals were collected from 81 participants in four sessions, with intervals of one, two, and three weeks between sessions, to evaluate the system’s performance across varying temporal conditions. To validate the feasibility of the proposed electrodes, simultaneous recordings were made using traditional Ag/AgCl electrodes, with results showing strong agreement (Pearson correlation = 0.9923, RMSE = 0.0174). Gramian Angular Field (GAF) representations were used to transform ECG signals into spatial images, which were then fed into a convolutional neural network (CNN). Results demonstrate that a short ECG segment of 10 heartbeats (approximately 10 s) can achieve competitive identification accuracy, with same-session performance reaching 94–98%. The system maintains moderate accuracy over short- to medium-term intervals (e.g., 58–65% for one to two weeks), with performance declining over extended time intervals (e.g., 47–50% for three weeks). These findings suggest the potential of ECG biometrics for real-world applications requiring rapid and non-invasive identification, such as security systems, wearable devices, and healthcare monitoring. Future work will focus on optimizing feature extraction and selection, and classification methods, expanding datasets, and addressing long-term signal variability to enhance system robustness. This work advances ECG biometrics by demonstrating the feasibility of short, fingertip-acquired signals for personal identification, paving the way for practical and user-friendly biometric systems.