Assumptions regarding eye morphology are implicit in iris recognition algorithms. The cornea is assumed to have little to no effect on the view of the iris texture when the eye gaze is non-frontal, the iris is assumed flat, and the eye is assumed to be imaged via an orthographic projection. If these assumptions hold, affine transformations may be used to rectify non-frontally posed images to a frontal view and the rubber sheet model may be accurately used to normalize the iris annulus to a rectangular image. This work examines how iris recognition performance degrades when the first two assumptions are violated. Using a computer renderable eye model, a data set is created varying the presence of the cornea and a parameterized non-planarity of the iris shape across a large range of eye gaze angles. Matching scores are created using a commercial matcher. When comparing the relative impact of each assumption violation, it is observed that iris non-planarity presents a more significant problem than corneal refractive distortion with regard to iris recognition accuracy in non-frontal images.