Age Estimation Guided Convolutional Neural Network for Age-Invariant Face Recognition

Tianyue Zheng, Weihong Deng, Jiani Hu; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 1-9

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 intra-class 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 person-specific 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.

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[bibtex]
@InProceedings{Zheng_2017_CVPR_Workshops,
author = {Zheng, Tianyue and Deng, Weihong and Hu, Jiani},
title = {Age Estimation Guided Convolutional Neural Network for Age-Invariant Face Recognition},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}