论文标题
有条件对抗网络的跨光谱周围识别
Cross-Spectral Periocular Recognition with Conditional Adversarial Networks
论文作者
论文摘要
这项工作解决了比较不同光谱中捕获的眼周图像的挑战，与在同一光谱中的工作相比，这众所周知，这会产生显着的性能下降。我们建议使用条件生成的对抗网络，该网络训练有素，可在可见的和近红外光谱之间进行眼周图像，从而在相同的光谱中进行生物识别验证。所提出的设置允许使用通常在单个频谱中进行操作的现有特征方法。使用基于手工制作的功能和CNN描述符的许多现成的眼周比较器进行识别实验。我们的实验将香港理工大学的跨光谱IRIS图像数据库（Polyu）作为基准数据集，表明，如果将两个图像转换为相同的频谱，则与从不同光谱中提取的匹配功能相比，跨光谱性能将显着提高。除此之外，我们还基于RESNET50体系结构微调了CNN，获得了EER = 1％的跨光谱周期性能，而GAR> 99％ @ far = 1％，这与Polyu数据库的最新时间可比。
This work addresses the challenge of comparing periocular images captured in different spectra, which is known to produce significant drops in performance in comparison to operating in the same spectrum. We propose the use of Conditional Generative Adversarial Networks, trained to con-vert periocular images between visible and near-infrared spectra, so that biometric verification is carried out in the same spectrum. The proposed setup allows the use of existing feature methods typically optimized to operate in a single spectrum. Recognition experiments are done using a number of off-the-shelf periocular comparators based both on hand-crafted features and CNN descriptors. Using the Hong Kong Polytechnic University Cross-Spectral Iris Images Database (PolyU) as benchmark dataset, our experiments show that cross-spectral performance is substantially improved if both images are converted to the same spectrum, in comparison to matching features extracted from images in different spectra. In addition to this, we fine-tune a CNN based on the ResNet50 architecture, obtaining a cross-spectral periocular performance of EER=1%, and GAR>99% @ FAR=1%, which is comparable to the state-of-the-art with the PolyU database.
