title: Iris recognition using possibilistic fuzzy matching on local features.
abstract: In this paper, we propose a novel possibilistic fuzzy matching strategy with invariant properties, which can provide a robust and effective matching scheme for two sets of iris feature points. In addition, the nonlinear normalization model is adopted to provide more accurate position before matching. Moreover, an effective iris segmentation method is proposed to refine the detected inner and outer boundaries to smooth curves. For feature extraction, the Gabor filters are adopted to detect the local feature points from the segmented iris image in the Cartesian coordinate system and to generate a rotation-invariant descriptor for each detected point. After that, the proposed matching algorithm is used to compute a similarity score for two sets of feature points from a pair of iris images. The experimental results show that the performance of our system is better than those of the systems based on the local features and is comparable to those of the typical systems.
authors: Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan.
authors: Chung-Chih
authors: CC
authors: Tsai
authors: Heng-Yi
authors: HY
authors: Lin
authors: Jinshiuh
authors: J
authors: Taur
authors: Chin-Wang
authors: CW
authors: Tao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007498:Iris / Q000033:anatomy & histology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D042262:Retinoscopy / Q000379:methods*; D013382:Subtraction Technique