Recognition Oriented Iris Image Quality Assessment in the Feature Space | |
Wang, Leyuan1,2,3; Zhang, Kunbo1,2,3 | |
2020-01-06 | |
会议日期 | 28 Sept.-1 Oct. 2020 |
会议地点 | Houston, TX, USA |
英文摘要 | A large portion of iris images captured in real world scenarios are poor quality due to the uncontrolled environment and the non-cooperative subject. To ensure that the recognition algorithm is not affected by low-quality images, traditional hand-crafted factors based methods discard most images, which will cause system timeout and disrupt user experience. In this paper, we propose a recognition-oriented quality metric and assessment method for iris image to deal with the problem. The method regards the iris image em-beddings Distance in Feature Space (DFS) as the quality metric and the prediction is based on deep neural networks with the attention mechanism. The quality metric proposed in this paper can significantly improve the performance of the recognition algorithm while reducing the number of images discarded for recognition, which is advantageous over hand-crafted factors based iris quality assessment methods. The relationship between Image Rejection Rate (IRR) and Equal Error Rate (EER) is proposed to evaluate the performance of the quality assessment algorithm under the same image quality distribution and the same recognition algorithm. Compared with hand-crafted factors based methods, the proposed method is a trial to bridge the gap between the image quality assessment and biometric recognition. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/45474] |
专题 | 自动化研究所_智能感知与计算研究中心 |
作者单位 | 1.Center for Research on Intelligent Perception and Computing 2.National Lab of Pattern Recognition 3.Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Wang, Leyuan,Zhang, Kunbo. Recognition Oriented Iris Image Quality Assessment in the Feature Space[C]. 见:. Houston, TX, USA. 28 Sept.-1 Oct. 2020. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论