Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification and at the same time, the difficulty in effectively representing such details in an image. Iris recognition illustrates work in computer vision, pattern recognition, and the man-machine interface. The purpose is real-time, high confidence recognition of a person's identity by mathematical analysis of the random patterns that are visible within the iris of an eye from some distance. Iris is a protected internal organ whose random texture is stable throughout life, it can serve as a kind of living password that one need not remember but one always carries along. Because the randomness of iris patterns has very high dimensionality, recognition decisions are made with confidence levels high enough to support rapid and reliable exhaustive searches through national-sized databases. Iris recognition has shown to be very accurate for human identification. This paper proposes a technique for iris pattern extraction utilizing the least significant bit-plane through binary morphology applied to the bit-plane and by evaluating the standard deviation of the image intensity along the vertical and horizontal axis, the pupillary boundary of the iris is determined. The limbic boundary is identified by adaptive thresholding method. Because the extraction approach restricts localization techniques to evaluating only bit-planes and standard deviations, iris pattern extraction is dependent on circular edge detection. The iris normalization was invariant for translation, rotation and scale after mapping into polar coordinates. Experiment and results show that the proposed method has an encouraging performance, shows 98.7% localization and normalization success and reduces the system operation time. The proposed method involves Bit plane slicing, Standard deviation windows, Adaptive thresholding, Normalization modules.