| Boosting multi-modal ocular recognition via spatial feature reconstruction and unsupervised image quality estimation | |
Yan ZH(闫紫徽)1,2 ; He LX(何凌霄)1,2; Wang YL(王云龙)1,2; Zhang KB(张堃博)1,2; Sun ZN(孙哲南)1,2
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| 发表期刊 | Machine Intelligence Research
 |
| 2023-01 | |
| 页码 | 已接收 |
| 摘要 | In the daily application of an iris-recognition-at-a-distance (IAAD) sys
tem, many ocular images of low quality are acquired. As the iris part of
these images is often not qualified for the recognition requirements, the
more accessible periocular regions are a good complement for recognition.
To further boost the performance of IAAD systems, a novel end-to-end
framework for multi-modal ocular recognition is proposed. The pro
posed framework mainly consists of iris/periocualr feature extraction
and matching, unsupervised iris quality assessment, and a score-level
adaptive weighted fusion strategy. First, ocular feature reconstruction
(OFR) is proposed to sparsely reconstruct each probe image by high
quality gallery images based on proper feature maps. Next, a brand
new unsupervised iris quality assessment method based on random mul
tiscale embedding robustness is proposed. Different from the existing
iris quality assessment methods, the quality of an iris image is mea
sured by its robustness in the embedding space. At last, the fusion
strategy exploits the iris quality score as the fusion weight to coalesce
the complementary information from the iris and periocular regions.
Extensive experimental results on ocular datasets prove that the pro
posed method is obviously better than unimodal biometrics, and the
fusion strategy can significantly improve the recognition performance. |
| 收录类别 | EI |
| 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 |
| 是否有论文关联数据集需要存交 | 否 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/51859 |
| 专题 | 模式识别实验室 |
| 通讯作者 | Wang YL(王云龙) |
| 作者单位 | 1.中科院自动化所 2.中国科学院大学 |
| 第一作者单位 | 中国科学院自动化研究所 |
| 通讯作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 GB/T 7714 | Yan ZH,He LX,Wang YL,et al. Boosting multi-modal ocular recognition via spatial feature reconstruction and unsupervised image quality estimation[J]. Machine Intelligence Research,2023:已接收. |
| APA | Yan ZH,He LX,Wang YL,Zhang KB,&Sun ZN.(2023).Boosting multi-modal ocular recognition via spatial feature reconstruction and unsupervised image quality estimation.Machine Intelligence Research,已接收. |
| MLA | Yan ZH,et al."Boosting multi-modal ocular recognition via spatial feature reconstruction and unsupervised image quality estimation".Machine Intelligence Research (2023):已接收. |
| 条目包含的文件 | 下载所有文件 | |||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Boosting_multi_modal（13329KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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