Iris Recognition Based on Using Ridgelet and Curvelet Transform

Biometric methods have been played important roles in personal recognition during last twenty years. These methods include the face recognition, finger print and iris recognition. Recently iris imaging has many applications in security systems. The aim of this paper is to design and implement a new iris recognition algorithm. In this paper, the new feature extraction methods according to ridgelet transform and curvelet transform for identifying the iris images are provided. At first, after segmentation and normalization the collarette area of iris images has been extracted. Then we improve the quality of image by using median filter, histogram equalization, and the two-dimensional (2D) Wiener filter as well. Finally the ridgelet transform and curvelet transform are applied for extracting features and then the binary bit stream vectors are generated. The Hamming distance (HD) between the input bit stream vector and stored vectors is calculated for iris identification. The experimental results show efficiency of our proposed method.