<p>The development of a safe and accurate biometric system presents a global challenge. Various biometric modalities have been discussed in the literature, the most popular being fingerprint, iris, and voice recognition. While these traditional methods can theoretically achieve a zero error rate, they are not secure. In contrast, a biometric retina identification system offers the highest level of security. This paper introduces an innovative biometric identification method leveraging deep learning and retinal data, explicitly employing the DenseNet architecture. To enhance the model performance, we employ the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which dynamically adjusts the learning rate, dropout rate, and the number of neurons in the Dense layer. Our research uses several publicly available databases, including the Retinal Identification Database (RIDB), Automated Retinal Image Analysis (ARIA), Structured Analysis of the Retina (STARE), Digital Retinal Images for Vessel Extraction (DRIVE), and Visual Acuity and Retinal Image Analysis (VARIA). Additionally, we have created a new retinal fundus image dataset using an EIDON non-mydriatic retinal camera, providing high-resolution and accurate imaging across multiple modalities. Obtained results present high performance, achieving accuracy rates of 100%, 100%, 99.9%, 100%, 100%, and 100% across the RIDB, ARIA, STARE, DRIVE, VARIA, and our newly collected datasets. These findings highlight the vital role that DenseNet plays in strengthening the security and reliability of personal identification systems. Furthermore, our research emphasizes the importance of the DenseNet architecture in improving the performance and dependability of biometric retina identification systems, thereby enabling secure identification for various applications.</p>