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Facial Landmark Detection Using Generative Adversarial Network Combined with Autoencoder for Occlusion
2020
Mathematical Problems in Engineering
The performance of the facial landmark detection model will be in trouble when it is under occlusion condition. In this paper, we present an effective framework with the objective of addressing the occlusion problem for facial landmark detection, which includes a generative adversarial network with improved autoencoders (GAN-IAs) and deep regression networks. In this model, GAN-IA can restore the occluded face region by utilizing skip concatenation among feature maps to keep more details.
doi:10.1155/2020/4589260
fatcat:dl72cim6djgp3dhyt6es3j4xb4