Authors: Edward Tan; Anto Satriyo Nugroho; Maulahikmah Galinium
Addresses: Department of Information Technology, Faculty of Engineering and Information Technology, Swiss German University, The Prominence Tower Alam Sutera, Tangerang, 15143, Indonesia ' Center for Information and Communication Technology, Agency for the Assessment and Application of Technology, Teknologi 3 Building, 3rd floor, Puspitek Serpong, Tangerang, 15314, Indonesia ' Department of Information Technology, Faculty of Engineering and Information Technology, Swiss German University, The Prominence Tower Alam Sutera, Tangerang, 15143, Indonesia
Abstract: The development of biometric authentication system should be followed by strengthening to spoofing attempts. Among various identifiers, iris has aroused many attentions due to its uniqueness and stability. Nevertheless, the use of iris for biometric authentication is accompanied by spoofing risk, for example using contact lens. In order to handle the spoofing attempts, its detection is an inevitable part of a recognition system, to reduce the risk of forging system. Cosmetic contact lens is one of most common spoofing materials which is hard to be detected. In this study, weighted local binary pattern (w-LBP) and simplified scale invariant feature transform (SIFT) descriptors were used to extract the feature of the iris, in which segmented using gradient magnitude and Fourier descriptor. Simplified SIFT descriptor is extracted at each pixel of iris image and being used to rank the local binary pattern (LBP) sequence of encoding. The features were then presented to support vector machine (SVM) classifier, for positive vs. negative classification. Positive class means that contact lens was used by a person, and vice versa. The experimental results showed that combining SIFT and w-LBP as features for SVM yielded an accuracy of 84%.
Keywords: biometrics; iris recognition; contact lens detection; classification.
International Journal of Biometrics, 2017 Vol.9 No.3, pp.243 - 252
Available online: 08 Sep 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article