Despite the great success achieved by convolutional neural networks (CNNs) in various image understanding tasks, it is still difficult for CNNs to be applied to vein recognition tasks due to the problems of insufficient training datasets, intra-class variations, and inter-class similarities. Besides, due to the essential requirement on the storage of millions of parameters for CNN, it is challenging to use a CNN for designing a vein-based embedded person identification system. In this paper, these two problems are addressed by learning a discriminative and compact vein recognition model. For the first problem, a hierarchical generative adversarial network (HGAN) consisting of a constrained CNN and a CycleGAN is proposed for data augmentation. Two similarity losses are defined for estimating the self-similarity and inter-class dissimilarity, and a CycleGAN model is properly trained with these two losses for better task-specific training sample generation. After obtaining a baseline vein recognition model fine-tuned on the augmented datasets, the existence of parameter redundancy in the over-parameterized network motivates the proposal of model compression by way of filter pruning and low rank approximation, thus making the compressed model more suitable for deployment on embedded systems. Through the vein recognition experiments with two different datasets and an additional palmprint recognition experiment, the proposed algorithms are shown to yield a highly compact model while keeping the accuracy acceptable for application.

中文翻译：

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使用 GAN 化样本学习紧凑静脉辨别模型


尽管卷积神经网络（CNN）在各种图像理解任务中取得了巨大成功，但由于训练数据集不足、类内变异和类间相似性等问题，CNN 仍然很难应用于静脉识别任务。此外，由于CNN对存储数百万个参数的基本要求，使用CNN来设计基于静脉的嵌入式人员识别系统具有挑战性。在本文中，通过学习有判别力且紧凑的静脉识别模型来解决这两个问题。对于第一个问题，提出了一种由约束 CNN 和 CycleGAN 组成的分层生成对抗网络（HGAN）用于数据增强。定义了两个相似性损失来估计自相似性和类间差异，并且使用这两个损失对 CycleGAN 模型进行了适当的训练，以更好地生成特定于任务的训练样本。在获得在增强数据集上微调的基线静脉识别模型后，超参数化网络中参数冗余的存在激发了通过滤波器剪枝和低秩近似进行模型压缩的建议，从而使压缩后的模型更适合在嵌入式系统上的部署。通过使用两个不同数据集的静脉识别实验和额外的掌纹识别实验，所提出的算法被证明可以产生高度紧凑的模型，同时保持应用程序可接受的精度。