Iris Segmentation and Recognization Using Log Gabor Filter and Curvelet Transform (International Journal of Engineering and Computer Science, 2013)
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 log-gabor filters and curvelet transform for identifying the iris images are provided. Iris recognition is annular region between the sclera and the pupil of the human eye. In this region, there exists an extraordinary texture including many prominent features, on which the recognition is mainly relied. In the existing approach adopted the Scale invariant Feature Transform (SIFT) to extract the local feature points in both Cartesian and polar coordinate systems. Since it is very likely that many local patterns of the iris are similar, the recognition accuracy of the system based on SIFT is not as good as those of the traditional methods. A novel fuzzy matching strategy with invariant properties, which can provide a robust and effective matching scheme for two sets of iris feature points and the nonlinear normalization model is adopted to provide more accurate position before matching. An effective iris segmentation method is proposed to refine the detected inner and outer boundaries to smooth curves. For feature extraction, instead of Log-Gabor filters we propose curvelet transform to detect the local feature points from the segmented iris image in the Cartesian coordinate system and to generate a rotation-invariant descriptor for each detected point. The proposed matching algorithm, which is based on the PFM method, is used to compare two sets of feature points by using the information comprising the local features and the position of each point