RANSAC lens boundary feature based kernel SVM for transparent contact lens detection

RANSAC lens boundary feature based kernel SVM for transparent contact lens detection

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Transparent contact lens spoofing has been demonstrated to hamper the overall performance of an iris recognition system. Achieving high detection accuracy with transparent lens is quite challenging using iris texture analysis based techniques. In this regard, the authors propose a supervised learning based transparent lens detection method by designing a novel set of features to describe the faint lens boundary. The input image is first segmented for extracting salient edge points in the sclera region of interest. RANSAC algorithm then performs circle fitting to generate the set of R-points for feature computation. The novel features such as concentricity parameter, R-point count, R-point intensity, R-circle radius, R-cluster count and circle genuineness are associated with distinctive range of values for each class. Experimental results obtained using the kernel-support vector machine (SVM) classifier show that the proposed method can achieve higher average detection accuracy as compared to state-of-the-art techniques: 90.63% (NotreDame I), 84.5% (NotreDame II), 83% (IIIT-D Cogent) and 86.27% (IIIT-D Vista).

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