人脸属性编辑的全局组织网络算法

In this paper, a novel global organization network for facial attribute editing was proposed based on a generative adversarial network. Facial attribute editing is to generate face images with desired attributes by combining the encoder-decoder structure with GAN. However, the traditional encoder-decoder structure has limited ability to reconstruct face images and edit attributes. Directly combining the encoder features with the attribute label, the method can result in poor attribute editing performance due to the incorporation of the encoder features, while, face restoration degree degrades because of the absence of the encoder features, and the two can not be balanced. Therefore, the global organization units (GOU) and U-shaped transferring method were proposed. U-shaped transferring method was arranged to change the traditional attribute flow mode and generate inverted states. Combining with the inverted states, the global organization unit was used to generate global state, build a bridge between the encoder and decoder, and help the decoder better integrate encoder features and attribute information. Meanwhile, in order to better fit the global organization unit, an encoder down-sampling was redesigned. Experimental results show that the proposed method can improve the ability of face reconstruction and attribute editing simultaneously.