论文标题
双歧节甘甘：gan概况的面部识别方式
Dual-discriminator GAN: A GAN way of profile face recognition
论文作者
论文摘要
当进行面部识别时，会出现大量的角度问题：目前，特征提取网络呈现特征向量，在许多情况下，额叶面和剖面面识别之间的特征差异很大。因此，最先进的面部识别网络将使用多个样本来实现相同的目标，以确保在训练过程中忽略了角度引起的特征向量差异。但是，还有另一种可用的解决方案，该解决方案是在识别之前生成带有剖面脸图像的正面脸部图像。在本文中，我们提出了一种基于生成对抗网络（GAN）的图像到图像形象面孔生成额叶面孔的方法。
A wealth of angle problems occur when facial recognition is performed: At present, the feature extraction network presents eigenvectors with large differences between the frontal face and profile face recognition of the same person in many cases. For this reason, the state-of-the-art facial recognition network will use multiple samples for the same target to ensure that eigenvector differences caused by angles are ignored during training. However, there is another solution available, which is to generate frontal face images with profile face images before recognition. In this paper, we proposed a method of generating frontal faces with image-to-image profile faces based on Generative Adversarial Network (GAN).
