Multi-Scale and Multi-Direction GAN for CNN-Based Single Palm-Vein Identification
Multi-Scale and Multi-Direction GAN for CNN-Based Single Palm-Vein Identification
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DOI：
10.1109/tifs.2021.3059340
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发表时间：
2021
影响因子：
6.8
通讯作者：
Huafeng Qin;M. El-Yacoubi;Yantao Li;Chongwen Liu
中科院分区：
文献类型：
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作者：
Huafeng Qin;M. El-Yacoubi;Yantao Li;Chongwen Liu
尽管深度神经网络在手部静脉识别方面取得了最新进展，但现有的解决方案假设有大量丰富的训练图像样本。因此，这些解决方案仍然缺乏从单个训练图像样本中提取鲁棒和有区别的手静脉特征的能力。为了克服这个问题，我们提出了一个单样本每人（SSPP）的手掌静脉识别方法，其中每个类只有一个单一的样本注册在画廊集进行训练。我们的方法名为MSMDGAN + CNN，由用于数据增强的多尺度和多方向生成对抗网络（MSMDGAN）和用于手掌静脉识别的卷积神经网络（CNN）组成。首先，一种新的数据增强方法，MSMDGAN，开发学习在一个单一的图像中的补丁的内部分布。提出的MSMDGAN由多个完全卷积的GAN组成，每个GAN负责学习图像中不同尺度和不同方向的补丁分布。其次，考虑到MSMDGAN产生的增强数据，我们设计了一个CNN用于单样本手掌静脉识别。在两个公开的手静脉数据库上的实验结果表明，MSMDGAN能够生成真实和多样化的样本，这反过来又提高了CNN的稳定性。在准确性方面，MSMDGAN + CNN优于其他代表性方法，并实现了最先进的识别结果。
Despite recent advances of deep neural networks in hand vein identification, the existing solutions assume the availability of a large and rich set of training image samples. These solutions, therefore, still lack the capability to extract robust and discriminative hand-vein features from a single training image sample. To overcome this problem, we propose a single-sample-per-person (SSPP) palm-vein identification approach, where only a single sample per class is enrolled in the gallery set for training. Our approach, named MSMDGAN + CNN, consists of a multi-scale and multi-direction generative adversarial network (MSMDGAN) for data augmentation and a convolutional neural network (CNN) for palm-vein identification. First, a novel data augmentation approach, MSMDGAN, is developed to learn the internal distribution of patches in a single image. The proposed MSMDGAN consists of multiple fully convolutional GANs, each of which is responsible for learning the patch distribution within an image at a different scale and at a different direction. Second, given the resulting augmented data by MSMDGAN, we design a CNN for single sample palm-vein recognition. The experimental results on two public hand-vein databases demonstrate that MSMDGAN is able to generate realistic and diverse samples, which, in turn, improves the stability of the CNN. In terms of accuracy, MSMDGAN + CNN outperforms other representative approaches and achieves state-of-the-art recognition results.