Deep learning-based palmprint recognition algorithms have obtained promising performance. However, the previous methods require a large amount of labeled samples, which are difficult to obtain. In this paper, a novel cross-dataset palmprint recognition method is proposed using as low as one labelled sample per subject in the target palmprint dataset based on adversarial domain adaptation. Two different palmprint datasets are adopted as source dataset and target dataset. The training samples from two datasets are grouped into four categories. MobileFaceNets-based deep hashing network (DHN) is introduced to extract discriminative features, which can improve the efficiencies of feature extracting and matching. To align the features in two datasets, a typical adversarial discriminator is augmented to distinguish between the four different categories. With adversarial learning, the target network is becoming adaptive to the unlabeled target palmprint images. Extensive experiments on the benchmarks including constrained and unconstrained palmprint databases demonstrate that our method can outperform the baseline models on cross-dataset palmprint verification and identification.

基于深度学习的掌纹识别算法已经获得了有希望的性能。然而，先前的方法需要大量的标记样品，难以获得。本文提出了一种新的跨数据集掌纹识别方法，该方法基于对抗域自适应，在目标掌纹数据集中每个对象使用低至一个标记的样本。采用两个不同的掌纹数据集作为源数据集和目标数据集。来自两个数据集的训练样本分为四个类别。引入了基于MobileFaceNets的深度哈希网络（DHN）来提取歧视性特征，从而可以提高特征提取和匹配的效率。为了使两个数据集中的特征对齐，增加了典型的对抗鉴别器以区分四个不同的类别。通过对抗性学习，目标网络开始适应未标记的目标掌纹图像。在包括受约束的掌纹数据库和不受约束的掌纹数据库在内的基准测试中，广泛的实验表明，我们的方法在跨数据集掌纹验证和识别方面可以胜过基线模型。