As facial appearance is subject to significant intra-class variations caused
by the aging process over time, age-invariant face recognition (AIFR) remains a
major challenge in face recognition community. To reduce the intra-class
discrepancy caused by the aging, in this paper we propose a novel approach
(namely, Orthogonal Embedding CNNs, or OE-CNNs) to learn the age-invariant deep
face features. Specifically, we decompose deep face features into two
orthogonal components to represent age-related and identity-related features.
As a result, identity-related features that are robust to aging are then used
for AIFR. Besides, for complementing the existing cross-age datasets and
advancing the research in this field, we construct a brand-new large-scale
Cross-Age Face dataset (CAF). Extensive experiments conducted on the three
public domain face aging datasets (MORPH Album 2, CACD-VS and FG-NET) have
shown the effectiveness of the proposed approach and the value of the
constructed CAF dataset on AIFR. Benchmarking our algorithm on one of the most
popular general face recognition (GFR) dataset LFW additionally demonstrates
the comparable generalization performance on GFR.

本文提出了一种新颖的方法（正交嵌入卷积神经网络，OE-CNNs）来学习年龄不变深度人脸特征，并构建了一个全新的大规模跨龄人脸数据集（CAF）用于增强该领域的研究和实验验证。实验结果表明，该方法在年龄不变人脸识别和一般人脸识别上表现具有可比性。