Retinal Identification Based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform
AbstractRetinal identification based on retinal vasculatures in the retina provides the most secure and accurate means of authentication among biometrics and has primarily been used in combination with access control systems at high security facilities. Recently, there has been much interest in retina identification. As digital retina images always suffer from deformations, the Scale Invariant Feature Transform (SIFT), which is known for its distinctiveness and invariance for scale and rotation, has been introduced to retinal based identification. However, some shortcomings like the difficulty of feature extraction and mismatching exist in SIFT-based identification. To solve these problems, a novel preprocessing method based on the Improved Circular Gabor Transform (ICGF) is proposed. After further processing by the iterated spatial anisotropic smooth method, the number of uninformative SIFT keypoints is decreased dramatically. Tested on the VARIA and eight simulated retina databases combining rotation and scaling, the developed method presents promising results and shows robustness to rotations and scale changes.
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Meng, X.; Yin, Y.; Yang, G.; Xi, X. Retinal Identification Based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform. Sensors 2013, 13, 9248-9266.
Meng X, Yin Y, Yang G, Xi X. Retinal Identification Based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform. Sensors. 2013; 13(7):9248-9266.Chicago/Turabian Style
Meng, Xianjing; Yin, Yilong; Yang, Gongping; Xi, Xiaoming. 2013. "Retinal Identification Based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform." Sensors 13, no. 7: 9248-9266.