This paper presents an enhanced iris verification system that improves segmentation and template matching by integrating additional evaluation metrics and registration screening. Traditional methods primarily use Hamming distance for template comparison, which can result in inadequate accuracy when distinguishing similar and dissimilar templates. To enhance performance, we incorporate Hamming distance, Jaccard distance, and Pearson correlation for a more comprehensive analysis, along with a variance-based enrollment screening mechanism to reject poorly segmented images. Evaluated on the CASIA-IrisV2 dataset, containing 1,200 images and 719,400 unique pairs, our results show significant improvements in balanced accuracy and recall, reducing false negatives. Although precision slightly declines, leading to increased false positives and affecting the \( F_{0.5} \) score, further refinement using variance thresholds mitigates this issue without compromising recall. Overall, our approach effectively addresses class imbalances and minimizes misclassification risks, resulting in a more reliable verification process capable of accommodating a broader range of input qualities while sustaining high overall performance.