论文标题
跨数据库棕榈识别的联合像素和功能对齐框架
A Joint Pixel and Feature Alignment Framework for Cross-dataset Palmprint Recognition
论文作者
论文摘要
基于深度学习的棕榈印刷识别算法已经显示出巨大的潜力。他们中的大多数主要专注于从同一数据集中识别样本。但是，它们可能不适合更方便的情况，即训练和测试的图像来自不同的数据集，例如由嵌入式终端和智能手机收集的。因此，我们提出了一种新型的关节像素和特征对齐（JPFA）框架，以解决此类跨数据素棕榈印刷识别方案。应用两个阶段对齐以在源和目标数据集中获得自适应特征。 1）采用深色样式传输模型将源图像转换为假图像，以减少数据集差距并在像素级别上执行数据扩展。 2）提出了一种新的深区自适应模型，以通过在特征级别对齐目标源和目标 - 捕获对的数据集特异性分布来提取自适应特征。足够的实验是在几个基准上进行的，包括受约束和不受限制的掌上数据库。结果表明，我们的JPFA优于其他模型来实现最新的模型。与基线相比，跨数据库识别的准确性提高了28.10％，跨数据库验证的误差率（EER）降低了4.69％。为了使我们的结果可再现，这些代码可在http://gr.xjtu.edu.cn/web/bell/resource上公开获得。
Deep learning-based palmprint recognition algorithms have shown great potential. Most of them are mainly focused on identifying samples from the same dataset. However, they may be not suitable for a more convenient case that the images for training and test are from different datasets, such as collected by embedded terminals and smartphones. Therefore, we propose a novel Joint Pixel and Feature Alignment (JPFA) framework for such cross-dataset palmprint recognition scenarios. Two stage-alignment is applied to obtain adaptive features in source and target datasets. 1) Deep style transfer model is adopted to convert source images into fake images to reduce the dataset gaps and perform data augmentation on pixel level. 2) A new deep domain adaptation model is proposed to extract adaptive features by aligning the dataset-specific distributions of target-source and target-fake pairs on feature level. Adequate experiments are conducted on several benchmarks including constrained and unconstrained palmprint databases. The results demonstrate that our JPFA outperforms other models to achieve the state-of-the-arts. Compared with baseline, the accuracy of cross-dataset identification is improved by up to 28.10% and the Equal Error Rate (EER) of cross-dataset verification is reduced by up to 4.69%. To make our results reproducible, the codes are publicly available at http://gr.xjtu.edu.cn/web/bell/resource.
