Iris recognition based on bidimensional empirical mode decomposition and fractal dimension
As the demand for information security increases, more attention is being paid to biometrics-based, automated personal identification. One of the most promising current biometric techniques is based on the human iris. This paper attempts to detect shape information from the iris by analyzing local intensity variations of an iris image. The methodology involves extraction of iris features using bidimensional empirical mode decomposition (BEMD) and fractal dimension. After the preprocessing procedure, the normalized effective iris image is decomposed into 2D intrinsic mode function (IMF) components at different spatial frequencies by bidimensional empirical mode decomposition. Then the texture features of each intrinsic mode function image are obtained via the differential box-counting method. To evaluate the efficacy of the proposed approach, three different similarity measures used in recognition are adopted. The experimental results using the CASIA and ICE iris databases show that the schema presented achieves promising results for iris recognition.
bidimensional empirical mode decomposition fractal dimension biometrics intrinsic mode function