High-quality facial-expression image generation for UAV pedestrian detection

For UAV pedestrian detection in the wild with perturbed parameters, such as lighting, distance, poor pixel and uneven distribution, traditional methods of image generation cannot accurately generate facial-expression images for UAV pedestrian detection. 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. 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 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.