LAE-GAN-Based Face Image Restoration for Low-Light Age Estimation

Abstract

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.

DFMFMA: Design of an efficient Deep learning model for age estimation from frontal Faces via Multimodal Feature representation & Multicomponent Augmented analysis

“…Another limitation of existing models is the limited performance under pose, illu-mination, and occlusion variations & sets [40], [41] and [42]. Address this limitation, some researchers have proposed data augmentation techniques, such as random crop-ping, flipping, and rotating, to improve the model's robustness to variations in facial images [43], [44] and [45].…”