论文标题
纹理意识到的自动编码器预训练和成对学习改进，以改善虹膜识别
Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition
论文作者
论文摘要
本文介绍了纹理意识到端到端可训练的IRIS识别系统，该系统专为诸如IRIS之类的数据集而设计，培训数据有限。我们以某些关键优化和建筑创新为基础，以先前的阶段学习框架为基础。首先，我们预先一个阶段1编码器网络，其无监督的自动编码器学习优化了，并在通常的重建损失之上具有附加的数据关系损失。数据关系损失使学习更好的纹理表示，这对于像IRIS这样的质地富含数据集至关重要。通过辅助授予任务进一步增强了1阶段特征表示的鲁棒性。这种预训练证明有益于有效地对受限制的IRIS数据集进行深入培训网络。接下来，在第二阶段有监督的改进中，我们为端到端可训练的IRIS识别系统设计了成对的学习体系结构。成对的学习包括训练管道本身内的虹膜匹配的任务，与通常的离线匹配相比，识别性能的显着提高。我们验证了三个公开可用的虹膜数据集中的模型，并且提议的模型始终优于传统和深度学习基线的数据集和跨数据库配置
This paper presents a texture aware end-to-end trainable iris recognition system, specifically designed for datasets like iris having limited training data. We build upon our previous stagewise learning framework with certain key optimization and architectural innovations. First, we pretrain a Stage-1 encoder network with an unsupervised autoencoder learning optimized with an additional data relation loss on top of usual reconstruction loss. The data relation loss enables learning better texture representation which is pivotal for a texture rich dataset such as iris. Robustness of Stage-1 feature representation is further enhanced with an auxiliary denoising task. Such pre-training proves beneficial for effectively training deep networks on data constrained iris datasets. Next, in Stage-2 supervised refinement, we design a pairwise learning architecture for an end-to-end trainable iris recognition system. The pairwise learning includes the task of iris matching inside the training pipeline itself and results in significant improvement in recognition performance compared to usual offline matching. We validate our model across three publicly available iris datasets and the proposed model consistently outperforms both traditional and deep learning baselines for both Within-Dataset and Cross-Dataset configurations
