J4 ›› 2014, Vol. 11 ›› Issue (3): 481-489.doi: 10.1016/S1672-6529(14)60060-3
Video Sequence-Based Iris Recognition Inspired byHuman Cognition Manner
Yuanning Liu1,2, Fei He1,2, Xiaodong Zhu1,2, Ying Chen1,2,3, |Ye Han1,2, Yanning Fu1,2
- 1. College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China
2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University,
Changchun 130012, P. R. China
3. College of Software, Nanchang HangKong University, Nanchang 330063, P. R. China
Video Sequence-Based Iris Recognition Inspired byHuman Cognition Manner
Yuanning Liu1,2, Fei He1,2, Xiaodong Zhu1,2, Ying Chen1,2,3, |Ye Han1,2, Yanning Fu1,2
- 1. College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China
2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University,
Changchun 130012, P. R. China
3. College of Software, Nanchang HangKong University, Nanchang 330063, P. R. China
摘要：
In video sequence-based iris recognition system, the problem of making full use of relationship and correlation among frames still remains to be solved. A brand new template level multimodal fusion algorithm inspired by human cognition manner is proposed. In that a non-isolated geometrical manifold, named Hyper Sausage Chain due to its sausage shape, is trained using the frames from a pattern class for representing an iris class in feature space. We can classify any input iris by observing which manifold it locates in. This process is closer to the function of human being, which takes ‘matter cognition’ instead of ‘matter classification’ as its basic principle. The experiments on self-developed JLUBR-IRIS dataset with several video sequences per person demonstrate the effectiveness and usability of the proposed algorithm for video sequence-based iris recognition. Fur-thermore, the comparative experiments on public CASIA-I and CASIA-V4-Interval datasets show that our method can also achieve improved performance of image-based iris recognition system, provided enough samples are involved in training stage.