当前位置: X-MOL 学术IEEE Trans. Inform. Forensics Secur. › 论文详情
Cycle Age-Adversarial Model Based on Identity Preserving Network and Transfer Learning for Cross-Age Face Recognition
IEEE Transactions on Information Forensics and Security ( IF 6.211 ) Pub Date : 2019-12-18 , DOI: 10.1109/tifs.2019.2960585
Lingshuang Du; Haifeng Hu; Yongbo Wu

Age variations bring a large challenge for face recognition tasks. Existing Cross-Age Face Recognition (CAFR) methods have two limitations. Firstly, many CAFR approaches require both age labels and identity labels for training. However, it is difficult to collect images under a large age span from each individual. Secondly, many works are based on the assumption that age and identity information are independent of each other, which may not satisfy various conditions. In this paper, a Cycle Age-Adversarial Model (CAAM) is proposed for CAFR, which only uses the age labels for training without considering independence hypothesis. CAAM includes two different branch networks. Firstly, the branch of Age-robust Feature Extracting Model (AFEM) is designed to adaptively learn age-invariant features by adversarial learning scheme, which includes an age discriminator network and a feature generator network. The age discriminator network is trained to discriminate the age information, and the generator extracts age-invariant features through adversarial learning with discriminator. Secondly, a branch of the Identity Preserving Network (IPN) is proposed to keep identity information, which introduces Unsupervised Identity Loss (UIL) to enlarge the inter-class distance, and decrease the loss of identity information in the learning process. Finally, the features of the two branches are cyclically optimized through minmizing Feature Consistency Loss (FCL), which integrates age invariance learning and identity discrimination learning into final feature representation. Different from existing CAFR networks, our adversarial learning strategy for age-robust feature learning can be generalized to other attributes including pose and expression. Moreover, we introduce cycle optimization strategy to merge the advantages of two branch networks, which is a novel strategy to fuse multi-task features. Extensive CAFR experiments performed on the benchmark MORPH Album2, CACD-VS and Cross Age LFW databases demonstrate the effectiveness and superiority of CAAM.
更新日期:2020-02-11

 

全部期刊列表>>
全球疫情及响应:BMC Medicine专题征稿
欢迎探索2019年最具下载量的化学论文
新版X-MOL期刊搜索和高级搜索功能介绍
化学材料学全球高引用
ACS材料视界
南方科技大学
x-mol收录
南方科技大学
自然科研论文编辑服务
上海交通大学彭文杰
中国科学院长春应化所于聪-4-8
武汉工程大学
课题组网站
X-MOL
深圳大学二维材料实验室张晗
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug