Contrastive Uncertainty learning for iris recognition with insufficient labeled samples | |
Wei Jianze1,2,3; He Ran1,3; Sun Zhenan1,3 | |
2021 | |
会议日期 | 04-07 August 2021 |
会议地点 | Shenzhen, China |
英文摘要 | Cross-database recognition is still an unavoidable challenge when deploying an iris recognition system to a new environment. In the paper, we present a compromise problem that resembles the real-world scenario, named iris recognition with insufficient labeled samples. This new problem aims to improve the recognition performance by utilizing partially-or un-labeled data. To address the problem, we propose Contrastive Uncertainty Learning (CUL) by integrating the merits of uncertainty learning and contrastive self-supervised learning. CUL makes two efforts to learn a discriminative and robust feature representation. On the one hand, CUL explores the uncertain acquisition factors and adopts a probabilistic embedding to represent the iris image. In the probabilistic representation, the identity information and acquisition factors are disentangled into the mean and variance, avoiding the impact of uncertain acquisition factors on the identity information. On the other hand, CUL utilizes probabilistic embeddings to generate virtual positive and negative pairs. Then CUL builds its contrastive loss to group the similar samples closely and push the dissimilar samples apart. The experimental results demonstrate the effectiveness of the proposed CUL for iris recognition with insufficient labeled samples. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48620] |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | Sun Zhenan |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 3.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Wei Jianze,He Ran,Sun Zhenan. Contrastive Uncertainty learning for iris recognition with insufficient labeled samples[C]. 见:. Shenzhen, China. 04-07 August 2021. |
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