当前位置: X-MOL 学术IEEE Trans. Pattern Anal. Mach. Intell. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Expression-Invariant Age Estimation Using Structured Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence ( IF 23.6 ) Pub Date : 2017-03-08 , DOI: 10.1109/tpami.2017.2679739
Zhongyu Lou , Fares Alnajar , Jose M. Alvarez , Ninghang Hu , Theo Gevers

In this paper, we investigate and exploit the influence of facial expressions on automatic age estimation. Different from existing approaches, our method jointly learns the age and expression by introducing a new graphical model with a latent layer between the age/expression labels and the features. This layer aims to learn the relationship between the age and expression and captures the face changes which induce the aging and expression appearance, and thus obtaining expression-invariant age estimation. Conducted on three age-expression datasets (FACES [1] , Lifespan [2] and NEMO [3] ), our experiments illustrate the improvement in performance when the age is jointly learnt with expression in comparison to expression-independent age estimation. The age estimation error is reduced by 14.43, 37.75 and 9.30 percent for the FACES, Lifespan and NEMO datasets respectively. The results obtained by our graphical model, without prior-knowledge of the expressions of the tested faces, are better than the best reported ones for all datasets. The flexibility of the proposed model to include more cues is explored by incorporating gender together with age and expression. The results show performance improvements for all cues.

中文翻译:

使用结构化学习的表情不变年龄估计

在本文中,我们研究并研究了面部表情对自动年龄估计的影响。与现有方法不同,我们的方法通过引入新的图形模型来共同学习年龄和表情,该图形模型在年龄/表达标签和要素之间具有潜在层。该层旨在了解年龄与表情之间的关系,并捕获诱发衰老和表情外观的面部变化,从而获得表情不变的年龄估计。在三个年龄表达数据集(FACES)上进行[1] , 寿命 [2] 和NEMO [3]),我们的实验表明,与不依赖于表达式的年龄估算相比,将与表达式一起学习的年龄可以提高性能。对于FACES,Lifespan和NEMO数据集,年龄估计误差分别降低了14.43%,37.75%和9.30%。通过我们的图形模型获得的结果,无需事先了解被测人脸的表情,对于所有数据集而言,其结果要比报道得最好的人要好。通过将性别,年龄和表情结合在一起,探索了所提议模型的灵活性,以包含更多线索。结果显示了所有提示的性能改进。
更新日期:2018-01-09
down
wechat
bug