  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.
