J4 ›› 2015, Vol. 12 ›› Issue (3): 504-517.doi: 10.1016/S1672-6529(14)60141-4
• 论文 • 上一篇
The Improved Characteristics of Bionic Gabor Representations by Combining with SIFT Key-points for Iris Recognition
Yuanning Liu1,2, Fei He1,2, Xiaodong Zhu1,2, Zhen Liu3, Ying Chen1,2,4, Ye Han1,2, Lijiao Yu1,2
- 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
3. Graduate School of Engineering, Nagasaki Institute of Applied Science, Japan
4. College of Software, Nanchang Hangkong University, Nanchang 330063, China
The Improved Characteristics of Bionic Gabor Representations by Combining with SIFT Key-points for Iris Recognition
Yuanning Liu1,2, Fei He1,2, Xiaodong Zhu1,2, Zhen Liu3, Ying Chen1,2,4, Ye Han1,2, Lijiao Yu1,2
- 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
3. Graduate School of Engineering, Nagasaki Institute of Applied Science, Japan
4. College of Software, Nanchang Hangkong University, Nanchang 330063, China
摘要：
Gabor filters are generally regarded as the most bionic filters corresponding to the visual perception of human. Their fil-tered coefficients thus are widely utilized to represent the texture information of irises. However, these wavelet-based iris representations are inevitably being misaligned in iris matching stage. In this paper, we try to improve the characteristics of bionic Gabor representations of each iris via combining the local Gabor features and the key-point descriptors of Scale Invariant Feature Transformation (SIFT), which respectively simulate the process of visual object class recognition in frequency and spatial domains. A localized approach of Gabor features is used to avoid the blocking effect in the process of image division, meanwhile a SIFT key point selection strategy is provided to remove the noises and probable misaligned key points. For the combination of these iris features, we propose a support vector regression based fusion rule, which may fuse their matching scores to a scalar score to make classification decision. The experiments on three public and self-developed iris datasets validate the discriminative ability of our multiple bionic iris features, and also demonstrate that the fusion system outperforms some state-of-the-art methods.