Abstract
Generative adversarial networks (GANs) can generate sentences that imitate real language features from discrete spaces, and the implicit expression space learned by training can continuously generate credible sentences. We solve the problem of face image restoration of different genders, different skin colors, and different hair colors through a method of generating face images, in which the face image generated by text description has a better generation effect. We propose a face image generation method (T-GAN) based on generating confrontational text. Due to the particulars of a face, a Word-long short-term memory model is used to extract the text features corresponding to the face. Then combined with the idea of generating a game against "game," a counter-text network is created, next a third type of input is added to the discriminator, and the real image with unmatched text is composed to strengthen the discriminator's training effect and force discrimination. The device determines whether the generated image conforms to the text description, so the discriminator can better learn the relationship between the text description and the image content. The experimental results show that, compared with other image generation methods, the face image generated by the proposed method can give high quality generated images with a better effect and less distortion, which can better maintain the original attributes of the image, has the use value, and increases the reduction degree by 3.84%. (c) 2022 SPIE and IS&T