Open AccessContact Lens Classification by Using Segmented Lens Boundary Features
Author(s) -
Nur Ariffin Mohd Zin,
Hishammuddin Asmuni,
Haza Nuzly Abdul Hamed,
Razib M. Othman,
Shahreen Kasim,
Rohayanti Hassan,
Zalmiyah Zakaria,
Rozaini Roslan
Publication year - 2018
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v11.i3.pp1129-1135
Subject(s) - lens (geology) , artificial intelligence , contact lens , computer science , iris (biosensor) , computer vision , boundary (topology) , sclera , pattern recognition (psychology) , segmentation , mathematics , optics , physics , ophthalmology , medicine , mathematical analysis , biometrics
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.