With the increasing need for security systems, iris recognition is one of the reliable solutions for biometrics-based identification systems. In general, an iris recognition algorithm includes four basic modules: image quality assessment, preprocessing, feature extraction, and matching. This work presents a whole iris recognition system, but particularly focuses on the image quality assessment and proposes an iris recognition scheme with an improved empirical mode decomposition (EMD) method. First, we assess the quality of each image in the input sequence and select clear enough iris images for the succeeding recognition processes. Then, an improved EMD (IEMD), a multiresolution decomposition technique, is applied to the iris pattern extraction. To verify the efficacy of the proposed approach, experiments are conducted on the public and freely available iris images from the CASIA and UBIRIS databases; three different similarity measures are used to evaluate the outcomes. The results show that the presented schemas achieve promising performance by those three measures. Therefore, the proposed method is feasible for iris recognition and IEMD is suitable for iris feature extraction.
With the increasing needs in security systems, iris recognition is reliable as one of the important solutions for biometrics-based identification systems. This work presents an effective approach for iris recognition by analyzing iris patterns. To improve the rate of recognition, we divide the normalized iris image into several regions to keep the iris image away from several noise factors, such as eyelids, eyelashes, and motion blur. For feature extraction, the local edge pattern (LEP) operator is designed to capture local characteristics of the iris image to produce discriminating texture features in every region. A resulting 2D feature vector is mapped into a low-dimensional subspace using two dimension linear discriminant analysis (2DLDA), and then the minimum distance classifier (MDC) is adopted for recognition. Experiments on the public and freely available iris images taken from the CASIA (Institute of Automation, Chinese Academy of Sciences) and UBIRIS databases confirm the advantage of the proposed approach in terms of speed and accuracy.