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. Meanwhile, self-attention mechanism that is effective in modeling long-range dependencies is employed to recover harmonious images for occluded faces. Deep regression networks are used to learn a nonlinear mapping from facial appearance to facial shape. Benefited from the mutual cooperation of GAN-IA and deep regression networks, a robust facial landmark detection model is achieved for the occlusion problem and the performance of the model achieves obviously improvement on challenging datasets.

中文翻译：

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生成对抗网络结合自动编码器的人脸标志检测

在遮挡条件下，面部界标检测模型的性能会出现问题。在本文中，我们提出了一个有效的框架，旨在解决面部界标检测的遮挡问题，其中包括具有改进的自动编码器（GAN-IA）的生成对抗网络和深度回归网络。在该模型中，GAN-IA可以通过使用特征图之间的跳过级联来保留更多细节，从而恢复被遮挡的面部区域。同时，有效的自我注意机制可用于建模远程依赖关系，以恢复被遮挡脸部的和谐图像。深度回归网络用于学习从面部外观到面部形状的非线性映射。受益于GAN-IA和深度回归网络的相互合作，