Iris verification systems are known for their high accuracy in biometric identification; however, false rejections and false acceptances remain critical challenges, particularly when relying solely on iris features. In this study, we propose a score fusion approach that combines iris and periocular verification systems to enhance performance and reduce false negatives. Our proposed method integrates the iris verification system with periocular features processed through an autoencoder. The final verification decision is made through a score fusion module using a multi-layer perceptron (MLP) for iris verification and a support vector classifier (SVC) for the combined scores. Evaluating our approach on the CASIA-Iris V2 dataset, we observed improvements in recall, F1-score, and balanced accuracy compared to baseline methods, with a recall increase of 21.55%, an F1-score increase of 4.45%, and balanced accuracy improvement of 8.96%. These results demonstrate the potential of integrating periocular features to enhance biometric verification systems.