Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition
Yitong Wang, Dihong Gong, Zheng Zhou, Xing Ji, Hao Wang, Zhifeng Li, Wei Liu, Tong Zhang
Abstract
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 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.
BibTeX
@inproceedings{eccv2018_orthogonaldeepfe,
title = {Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition},
author = {Yitong Wang and Dihong Gong and Zheng Zhou and Xing Ji and Hao Wang and Zhifeng Li and Wei Liu and Tong Zhang},
booktitle = {ECCV 2018},
year = {2018}
}