In this paper, a new shape analysis approach for iris recognition is proposed. First, the extracted iris images from eye portrait are enhanced by image deblurring filter which computes restoration using FFT-based Tikhonov filter with the identity matrix as the regularization operator. This procedure produces a smooth image in which shape of pigmented fibro vascular tissue known as Stroma is depicted easily. Then, an adaptive filter is defined to extract these shapes. In the next step, shape analysis techniques are applied in order to extract robust features from contour of the shapes such as support functions and radius vectors. These features are invariant under iris localization and mapping. Finally, a feature strip code is defined for every iris image. Introduced algorithm is applied to UBIRIS databank. Experimental results show efficiency of the proposed method by achieving an accuracy of 95.08% on first session of UBIRIS.