Age Regression with Specific Facial Landmarks by Dual Discriminator Adversarial Autoencoder

Abstract

Facial age conversion is to generate faces of different age groups from the input face and retain the characteristics of the original face. Most of the existing methods are exploring the aging of the face, while ignoring the rejuvenation. In addition to improving aging, we will also explore the regression of human faces. Due to the lack of images of the same person in a longer age range, it becomes a challenging task. Since the generated faces are relatively unreal, we developed a novel model based on Conditional Adversarial Autoencoder (CAAE). This model uses two discriminators to generate a more realistic image. Furthermore, by considering specific facial landmarks where the face shape has changed greatly in different age groups, the face shape belonging to the corresponding age group can be obtained. Moreover, the collected database is divided into different races for training to improve the age development of different races.

Half Wavelet Attention on M-Net+ for Low-Light Image Enhancement

“…iterations. For data augmentation [23,24,25,26], randomly horizontal and vertical flips are adopted. We use Adam optimizer with the initial learning rate of 1 × 10 −4 and decrease to 1 × 10 −6 by the consine annealing strategy.…”