Ocular biometrics in the visible spectrum has emerged as an area of significant research activity. In this paper, we propose two convolution-based models for verifying a pair of periocular images containing the iris, and compare the two approaches amongst each other as well as with a baseline model. In the first approach, we perform deep learning in an unsupervised manner using a stacked convolutional architecture, using external models learned a-priori on external facial and periocular data, on top of the baseline model applied on the provided data, and apply different score fusion models. In the second approach, we again use a stacked convolution architecture; but here, we learn the feature vector in a supervised manner. We obtain an AUROC of 0.946 and 0.981, and EER of 0.092 and 0.066, for the two models respectively. We further combine the two models, and observe the combined model to deliver the best performance in case the both the images arise from the same device type, but not necessarily so otherwise, obtaining a AUROC of 0.985 and EER of 0.057. Given the significant performance our methodology yields, our system can be used in real-life applications with minimal error.