Iris recognition based on Hilbert-Huang transform

As a reliable approach for human identification, iris recognition has received increasing attention in recent years. This paper proposes a new analysis method for iris recognition based on Hilbert–Huang transform (HHT). We first divide a normalized iris image into several subregions. Then the main frequency center information ased on HHT of each subregion is employed to form the feature vector. The proposed iris recognition method has nice properties, such as translation invariance, scale invariance, rotation invariance, illumination invariance and robustness to high frequency noise. Moreover, the xperimental results on the CASIA iris database which is the largest publicly available iris image data sets show that the performance of the proposed method is encouraging andcomparable to the best iris recognition algorithm found in the current literature.