Anca Ignat, Ioan Păvăloi
Conference: 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems,16-18 Septembre 2020
The object of interest of this paper is an original method based on SURF descriptors and texture features applied on iris recognition. Our approach uses SURF method in two stages. In the first step, few keypoints are generated and then combining these results with texture based results allows us perform the second step. In the second stage, one applies again SURF, this time with much more descriptors, thus improving the recognition rate. In our experiments we have tested the influence on the recognition rate of different parameters involved in both the SURF based feature extraction process and the keypoint matching scheme. For tests we employed two iris datasets, namely UPOL and UBIRIS. We use Local Binary Patterns (LBP) and Dual Tree Complex Wavelet Transform (DTCWT) for texture characterization. We experimentally prove that the proposed approach improves the results obtained by applying these methods separately. We show that our method can be successfully used as a filter that reduces the search space for image recognition, the output of this filtering process can be then processed using computationally expensive methods.

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