Alignment Free and Distortion Robust Iris Recognition | |
Min Ren1,2; Caiyong Wang1,2; Yunlong Wang2; Zhenan Sun2; Tieniu Tan2 | |
2019-06 | |
会议名称 | International Conference on Biometrics (ICB) |
会议日期 | 2019-6-4 |
会议地点 | Crete, Greece |
摘要 | Iris recognition is a reliable personal identification method but there is still much room to improve its accu- racy especially in less-constrained situations. For example, free movement of head pose may cause large rotation dif- ference between iris images. And illumination variations may cause irregular distortion of iris texture. To match intra-class iris images with head rotation robustly, the exist- ing solutions usually need a precise alignment operation by exhaustive search within a determined range in iris image preprosessing or brute-force searching the minimum Ham- ming distance in iris feature matching. In the wild envi- roments, iris rotation is of much greater uncertainty than that in constrained situations and exhaustive search within a determined range is impracticable. This paper presents a unified feature-level solution to both alignment free and distortion robust iris recognition in the wild. A new deep learning based method named Alignment Free Iris Net- work (AFINet) is proposed, which utilizes a trainable VLAD (Vector of Locally Aggregated Descriptors) encoder called NetVLAD [18] to decouple the correlations between local representations and their spatial positions. And deformable convolution [5] is leveraged to overcome iris texture distor- tion by dense adaptive sampling. The results of extensive experiments on three public iris image databases and the simulated degradation databases show that AFINet signifi- cantly outperforms state-of-art iris recognition methods. |
语种 | 英语 |
七大方向——子方向分类 | 生物特征识别 |
国重实验室规划方向分类 | 视觉信息处理 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50605 |
专题 | 模式识别实验室 |
作者单位 | 1.University of Chinese Academy of Sciences 2.CRIPAC, NLPR, CASIA |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Min Ren,Caiyong Wang,Yunlong Wang,et al. Alignment Free and Distortion Robust Iris Recognition[C],2019. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ICB-0113.pdf(4851KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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