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Accurate iris recognition from the distantly acquired face or eye images under less constrained environments require development of specialized strategies which can accommodate for significant image variations (e.g., scale, rotation, translation) and influence from multiple noise sources. A set of coordinate-pairs, which is referred to as geometric key in this paper is randomly generated and exclusively assigned to each subject enrolled into the system. Such geometric key uniquely defines the way how the iris features are encoded from the localized iris region pixels. Such iris encoding scheme involves computationally efficient and fast comparison operation on the locally assembled image patches using the locations defined by the geometric key. The image patches involved in such operation can be more tolerant to the noise. Scale and rotation changes in the localized iris region can be well accommodated by using the transformed geometric key. The binarized encoding of such local iris features still allows efficient computation of their similarity using Hamming distance. The superiority of the proposed iris encoding and matching strategy is ascertained by providing comparison with several state-of-the-art iris encoding and matching algorithms on three publicly available databases: UBIRIS.v2, FRGC, CASIA.v4-distance, which suggests the average improvements of 36.3%, 32.7%, and 29.6% in equal error rates, respectively, as compared with several competing approaches.