Age estimation is applicable in various fields, and among them, research on age estimation using human facial images, which are the easiest to acquire, is being actively conducted. Since the emergence of deep learning, studies on age estimation using various types of convolutional neural networks (CNN) have been conducted, and they have resulted in good performances, as clear images with high illumination were typically used in these studies. However, human facial images are typically captured in low-light environments. Age information can be lost in facial images captured in low-illumination environments, where noise and blur generated by the camera in the captured image reduce the age estimation performance. No study has yet been conducted on age estimation using facial images captured under low light. In order to overcome this problem, this study proposes a new generative adversarial network for low-light age estimation (LAE-GAN), which compensates for the brightness of human facial images captured in low-light environments, and a CNN-based age estimation method in which compensated images are input. When the experiment was conducted using the MORPH, AFAD, and FG-NET databases—which are open databases—the proposed method exhibited more accurate age estimation performance and brightness compensation in low-light images compared to state-of-the-art methods.

中文翻译：

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用于弱光年龄估计的基于 LAE-GAN 的人脸图像恢复

年龄估计适用于各个领域，其中，使用最容易获取的人脸图像进行年龄估计的研究正在积极进行。自从深度学习出现以来，已经进行了使用各种类型的卷积神经网络 (CNN) 进行年龄估计的研究，并且它们取得了良好的性能，因为这些研究中通常使用具有高照度的清晰图像。然而，人脸图像通常是在低光环境中捕获的。在低照度环境中捕获的面部图像中可能会丢失年龄信息，其中摄像头在捕获的图像中产生的噪声和模糊会降低年龄估计性能。尚未对使用在低光下拍摄的面部图像进行年龄估计进行研究。为了克服这个问题，本研究提出了一种新的用于弱光年龄估计的生成对抗网络 (LAE-GAN)，它可以补偿在弱光环境中捕获的人脸图像的亮度，以及一种基于 CNN 的年龄估计方法，其中输入补偿图像. 当使用 MORPH、AFAD 和 FG-NET 数据库进行实验时——这是开放的数据库——与最先进的方法相比，所提出的方法在低光图像中表现出更准确的年龄估计性能和亮度补偿。