Iris recognition is widely regarded as an efficient biometric technique due to the uniqueness and persistence of iris features. This recognition system has shown high accuracy and reliability compared to other biometric methods. However, extracting the relevant features from iris images poses a significant challenge. In this experimental research, it is found that fusing multiple wavelet features leads to improved accuracy, compared to using a single feature-based iris verification method. Specifically, support vector machines (SVM) as the classification algorithm is employed in this work. In this study, the accuracy of 19 types of different wavelets is assessed and the performance of the coiflet4 wavelet at the second-level horizontal component is specifically observed, which achieved an accuracy of 91.8%. On the other hand, the lowest accuracy was recorded for the bior1.3 wavelet at the fourth-level horizontal component, with a score of 65.6%. These findings emphasise the importance of selecting appropriate wavelets for achieving greater accuracy. This experimental setup yielded an accuracy of 98.62% for the CASIA database and 97.87% for the UBIRIS database. These accuracies indicate the effectiveness of utilizing multiple wavelet features and SVM-based classification in iris-based person verification. By exploiting the fusion of multiple wavelet characteristics, the recognition system’s ability to distinguish is enhanced, resulting in improved precision and dependability in the verification of individuals based on their iris patterns. The main work of this research is to evaluate the performance of half-iris-based verification. The goal is to address the challenges faced due to occlusion and rotational variations between the reference and test iris images. The analysis focuses on half iris verification with rotational variations of ±5◦,±10◦,±15◦ between the reference and test iris images. For the half iris, the strip size is reduced to 20x120 pixels, reflecting the analysis of only a portion of the iris and creating a more compact representation of its features. Multiple wavelet features and half iris-based person verification using SVM classifier techniques are applied here. The proposed technique for rotational invariant half-iris-based person verification achieves an accuracy of 98.59% for the CASIA database and 97.65% for the UBIRIS database. The half-iris-based verification results are very close to the full-iris-based verification results. The overall verification accuracy is far better compared to the reported results. The maximum verification accuracy reported for the CASIA database is 96.13%, whereas, we have achieved 98.59% accuracy for half iris with less number of features.