Vein recognition has received increasing attention due to its high security and privacy. Recently, deep neural networks such as Convolutional neural networks (CNN) and Transformers have been introduced for vein recognition and achieved state-of-the-art performance. Despite the recent advances, however, existing solutions for finger-vein feature extraction are still not optimal due to scarce training image samples. To overcome this problem, in this paper, we propose an adversarial masking contrastive learning (AMCL) approach, that generates challenging samples to train a more robust contrastive learning model for the downstream palm-vein recognition task, by alternatively optimizing the encoder in the contrastive learning model and a set of latent variables. First, a huge number of masks are generated to train a robust generative adversarial network (GAN). The trained generator transforms a latent variable from the latent variable space into a mask space. Then, we combine the trained generator with a contrastive learning model to obtain our AMCL, where the generator produces challenging masking images to increase the contrastive loss and the contrastive learning model is trained based on the harder images to learn a more robust feature representation. After training, the trained encoder in the contrastive learning model is combined with a classification layer to build a classifier, which is further fine-tuned on labeled training data for vein recognition. The experimental results on three databases demonstrate that our approach outperforms existing contrastive learning approaches in terms of improving identification accuracy of vein classifiers and achieves state-of-the-art recognition results.

翻译：静脉识别因其高安全性和隐私保护而受到越来越多的关注。近年来，卷积神经网络（CNN）和Transformer等深度神经网络被引入静脉识别领域，并取得了最先进的性能。然而，尽管取得了这些进展，现有的指静脉特征提取方法由于训练图像样本稀缺仍非最优。为解决此问题，本文提出一种对抗遮蔽对比学习（AMCL）方法，通过交替优化对比学习模型中的编码器和一组潜在变量，生成具有挑战性的样本，以训练更鲁棒的对比学习模型，用于下游掌静脉识别任务。首先，生成大量遮蔽图以训练一个鲁棒的生成对抗网络（GAN）。训练后的生成器将潜在变量空间中的潜在变量转换为遮蔽空间。然后，我们将训练好的生成器与对比学习模型结合，得到AMCL，其中生成器产生具有挑战性的遮蔽图像以增加对比损失，而对比学习模型则基于更困难的图像进行训练，以学习更鲁棒的特征表示。训练完成后，对比学习模型中的编码器与分类层结合构建分类器，并在标注训练数据上进一步微调，用于静脉识别。在三个数据库上的实验结果表明，我们的方法在提高静脉分类器识别准确率方面优于现有对比学习方法，并达到了最先进的识别结果。