Contact Lens Classification by Using Segmented Lens Boundary Features

Nur Ariffin Mohd Zin, Hishammuddin Asmuni, Haza Nuzly Abdul Hamed, Razib M. Othman, Shahreen Kasim, Rohayanti Hassan, Zalmiyah Zakaria, Rosfuzah Roslan


Recent studies have shown that the wearing of soft lens may lead to performance degradation with the increase of false reject rate. However, detecting the presence of soft lens is a non-trivial task as its texture that almost indiscernible. In this work, we proposed a classification method to identify the existence of soft lens in iris image. Our proposed method starts with segmenting the lens boundary on top of the sclera region. Then, the segmented boundary is used as features and extracted by local descriptors. These features are then trained and classified using Support Vector Machines. This method was tested on Notre Dame Cosmetic Contact Lens 2013 database. Experiment showed that the proposed method performed better than state of the art methods.


Contact lens classification; local descriptor; Support Vector Machines

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