In this paper, circular contourlet transform (CCT) is proposed, designed and realized. As in the classical contourlet transform (CT), a double filter bank structure is also considered in this work but in different manners. A circularly-decomposed filter bank is first used to capture the points of discontinuities in the image edges, and then followed by a directional filter bank to obtain smoothed contours. The resulting CCT contains a critically sampled filter bank that decomposes images into any power of two's number of directional subbands at multiple scales. The designed CCT is realized by 2-D lattice allpass sections with separable and non-separable 2-D functions of z 1 and z 2. The resulting structure preserves both modularity and regularity properties which are suitable for VLSI implementations. Objectively, the performances of the realized CCT are tested and proved to be better than the classical CT in detail image preservation. The resulting subband images also indicate the superiority of the proposed CCT.

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In this paper, a new personal identification method based on unconstrained iris recognition is presented. We apply a nontraditional step for feature extraction where a new circular contourlet filter bank is used to capture the iris characteristics. This idea is based on a new geometrical image transform called the circular contourlet transform (CCT). An efficient multilevel and multidirectional contourlet decomposition method is needed to form a reduced-length quantized feature vector with improved performance. The CCT transform provides both multiscale and multioriented analysis of iris features. Circular contourlet-like mask filters can be used with shapes just like the 2D circular-support regions in different scales and directions. A reduced recognition system is realized using a single branch of the whole decomposition bank, highlighting a system realization with lower complexity and fewer computations. In the proposed recognition system, only five out of seven elements of the gray level cooccurrence matrix are required in the creation of the feature vector, which leads to a further reduction in computations. In addition, the highly discriminative frequency regions due to the use of circular-support decompositions can result in highly accurate feature vectors, reflecting good recognition rates for the proposed system. It is shown that the proposed system has encouraging performance in terms of high recognition rates and a reduced number of elements of the feature vector. This reflects reliable and rapid recognition properties. In addition, some promising characteristics of the system are apparent since it can efficiently be realized with lower computation complexity.