Age Estimation Guided Convolutional Neural Network for Age-Invariant Face Recognition
Abstract
While very promising results have been shown on face recognition related problems, age-invariant face recognition still remains a challenge. Facial appearance of a person changes over time, which results in significant intraclass variations. In order to address this problem, we propose a novel deep face recognition network called age estimation guided convolutional neural network (AE-CNN) to separate the variations caused by aging from the personspecific features which are stable. The carefully designed CNN model can learn age-invariant features for face recognition. To the best of our knowledge, this is the first attempt to use age estimation task for obtaining age-invariant features. Extensive results on two well-known public domain face aging datasets: MORPH Album 2 and CACD show the effectiveness of the proposed approach.
Cite
Text
Zheng et al. "Age Estimation Guided Convolutional Neural Network for Age-Invariant Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017. doi:10.1109/CVPRW.2017.77Markdown
[Zheng et al. "Age Estimation Guided Convolutional Neural Network for Age-Invariant Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017.](https://mlanthology.org/cvprw/2017/zheng2017cvprw-age/) doi:10.1109/CVPRW.2017.77BibTeX
@inproceedings{zheng2017cvprw-age,
title = {{Age Estimation Guided Convolutional Neural Network for Age-Invariant Face Recognition}},
author = {Zheng, Tianyue and Deng, Weihong and Hu, Jiani},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2017},
pages = {503-511},
doi = {10.1109/CVPRW.2017.77},
url = {https://mlanthology.org/cvprw/2017/zheng2017cvprw-age/}
}