High-quality facial-expression image generation for UAV pedestrian detection - Tang, Yumin; Fan, Jing; Qu, Jinshuai (Frontiers in Space Technologies, 2022)
For facial-expression recognition in the wild with perturbed parameters, such as lighting, race, and pose, traditional methods of image generation and enhancement cannot accurately generate facial-expression images. In this study, we propose an improved PR-SGAN (perceptual-remix-star generative adversarial network) method, which combines the improved interpolation method, perceptual loss function, and StarGAN to achieve high-quality facial-expression image generation. The experimental results show that the proposed method for discriminator-parameter update improves the generated facial-expression images in terms of image-generation evaluation indexes (5.80 dB in PSNR and 24% in SSIM); the generated images for the generator-parameter update have high robustness against color. Compared to the traditional StarGAN method, the generated images are significantly improved in high-frequency details and textures.