论文标题
年龄差距减少剂，用于识别年龄分离的面孔
Age Gap Reducer-GAN for Recognizing Age-Separated Faces
论文作者
论文摘要
在本文中，我们提出了一种新颖的算法，用于匹配面孔与年龄进程引起的时间变化。拟议的生成对抗网络算法是一个统一的框架，结合了面部年龄估计和年龄分离的面部验证。这种方法的关键思想是通过调节受试者性别和需要进步的目标年龄段的输入图像来了解整个时间的年龄变化。损失函数解释了原始图像和生成的面部图像之间的年龄差距以及保持身份。视觉保真度和定量评估都证明了所提出的体系结构在不同面部年龄数据库中对年龄分离的面部识别的功效。
In this paper, we propose a novel algorithm for matching faces with temporal variations caused due to age progression. The proposed generative adversarial network algorithm is a unified framework that combines facial age estimation and age-separated face verification. The key idea of this approach is to learn the age variations across time by conditioning the input image on the subject's gender and the target age group to which the face needs to be progressed. The loss function accounts for reducing the age gap between the original image and generated face image as well as preserving the identity. Both visual fidelity and quantitative evaluations demonstrate the efficacy of the proposed architecture on different facial age databases for age-separated face recognition.
