收稿日期: 2020-03-23
网络出版日期: 2020-09-10
基金资助
上海大学电影学高峰学科和上海电影特效工程技术研究中心资助项目(16dz2251300)
Method for improving age estimation using age editing
Received date: 2020-03-23
Online published: 2020-09-10
人脸年龄估计(face age estimation)作为一种新兴的生物特征识别技术, 是计算机视觉中一个经典的学习问题. 基于生成对抗网络(generative adversarial network, GAN) 强大的数据生成能力, 提出了一种利用年龄编辑改进年龄估计的方法, 并 通过分阶段联合训练年龄编辑网络 StarGAN 和年龄估计软阶段回归网络 (soft stagewise regression network, SSR-Net), 扩增适用于年龄估计的训练数据. 实验证明, 在非受限条件下, 该方法取得了年龄估计较好的结果.
张辰昱, 徐树公, 黄剑波 . 一种利用年龄编辑改进年龄估计的方法[J]. 上海大学学报(自然科学版), 2021 , 27(1) : 28 -38 . DOI: 10.12066/j.issn.1007-2861.2250
As an emerging biometric identification technology, face age estimation is a classic learning problem in computer vision. Based on the powerful data generation ability of a generative adversarial network (GAN), a method to improve age estimation by using age editing is proposed. The training data suitable for age estimation were amplified by combining the training age editing network-StarGAN and the age estimation network-soft stagewise regression network (SSR-Net), in stages. Experimental results show that the method achieves the better age estimation results under unrestricted conditions.
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