Progressive Face Age Synthesis Algorithm Based on Generative Adversarial Network
杨晓雨 1王爱侠 1杨钢 1李晶皎1
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作者信息
1. 东北大学 信息科学与工程学院,辽宁 沈阳 110819
折叠
摘要
人脸年龄合成(face age synthesis,FAS)的目标是根据源人脸图像合成指定年龄人脸图像,同时保留人脸的个人特征和身份信息.针对年龄变换时无关特征容易改变和产生伪影鬼影的问题,提出一种基于生成对抗网络的人脸年龄渐进合成算法.采用基于门控循环单元的年龄编辑模块自适应地过滤或加入特征,并使用属性解耦模块在潜在空间进行对抗学习,通过生成器和判别器的对抗策略保证了真实自然的人脸合成,使用年龄分类约束拟合特定年龄分布,为了保证年龄无关属性的保留,还在生成对抗网络中引入了重建学习.在跨年龄名人数据集(cross-age celebrity dataset,CACD)下的实验结果表明,对比其他基于条件生成对抗网络的算法,提出的算法生成的人脸图像伪影失真有所减少,年龄显著性增强,具有较好的年龄准确性和较高的身份一致性.
Abstract
The goal of face age synthesize(FAS)is to synthesize face images of specified ages based on the source face image,while preserving personal characteristics and identity information of the face.To solve the problem that irrelevant features are easy to change and artifact ghosting occurs when age is changed,a progressive face age synthesis algorithm based on generative adversarial network is proposed.The age editing module based on gate recurrent unit is used to filter or add features adaptively,and attribute decoupling module is used for adversarial learning in the latent space.Through the adversarial strategy of generator and discriminator,the real and natural face synthesis is guaranteed.The age classification constraint is used to fit the specific age distribution.In order to preserve age-independent properties,reconstruction learning is also introduced into generative adversarial network.Experimental results on CACD dataset show that,compared with other algorithms based on conditional generative adversarial network,the proposed algorithm has reduced artifacts and distortions,enhanced age significance,and has better age accuracy and higher identity consistency.
关键词
人脸年龄合成/生成对抗网络/属性解耦/潜在空间/门控循环单元/重建学习
Key words
face age synthesis/generative adversarial network/attribute decoupling/latent space/gate recurrent unit/reconstruction learning
维普
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