Context: Authentication systems are integral to ensuring secure and reliable access to digital and physical infrastructures. Traditional biometric authentication methods, such as iris recognition, facial recognition, and fingerprint recognition, each have their own strengths and limitations. Among these, iris recognition stands out for its high accuracy and low error rates, even in large-scale systems. Recent studies indicate that iris recognition has a false acceptance rate (FAR) as low as 0.0001%, compared to fingerprint recognition, which can have a FAR of up to 0.1%, and facial recognition, which can reach up to 1% under similar conditions. Objective: This study presents a segmentation-free, end-to-end approach for iris-based authentication, leveraging deep convolutional neural networks (CNNs) for feature extraction and classification. A proof of concept was conducted using the CASIA-Thousand-IRIS dataset to evaluate the feasibility of the proposed method. Preliminary results show a testing accuracy of 92.5%, demonstrating the viability of the proposed approach. Conclusions: The research presents the potential of applying blockchain to securely and decentrally manage identities, ensuring both enhanced accuracy and security for biometric authentication.