# Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition

arXiv: https://arxiv.org/abs/1810.07599 · HTML: https://mlsanity.com/paper/1810.07599/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2018.

## Built on by (most cited first, 20 of 20)

- [SynFace: Face Recognition with Synthetic Data](https://mlsanity.com/paper/2108.07960/) (2021)
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- [When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework](https://mlsanity.com/paper/2103.01520/) (2021)
- [LARNet: Lie Algebra Residual Network for Face Recognition](https://mlsanity.com/paper/2103.08147/) (2021)
- [One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training](https://mlsanity.com/paper/2308.07934/) (2023)
- [PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition](https://mlsanity.com/paper/2107.11721/) (2021)
- [When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark](https://mlsanity.com/paper/2210.09835/) (2022)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [Improving Face Recognition with Large Age Gaps by Learning to Distinguish Children](https://mlsanity.com/paper/2110.11630/) (2021)
- [Distortion-Disentangled Contrastive Learning](https://mlsanity.com/paper/2303.05066/) (2023)
- [From Age Estimation to Age-Invariant Face Recognition: Generalized Age Feature Extraction Using Order-Enhanced Contrastive Learning](https://mlsanity.com/paper/2501.01760/) (2025)
- [Information Maximization for Extreme Pose Face Recognition](https://mlsanity.com/paper/2209.03456/) (2022)
- [OCHID-Fi: Occlusion-Robust Hand Pose Estimation in 3D via RF-Vision](https://mlsanity.com/paper/2308.10146/) (2023)
- [Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval](https://mlsanity.com/paper/2407.01810/) (2024)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)
- [CCFace: Classification Consistency for Low-Resolution Face Recognition](https://mlsanity.com/paper/2308.09230/) (2023)
- [Age-Invariant Face Embedding using the Wasserstein Distance](https://mlsanity.com/paper/2305.02745/) (2023)
- [Human Gait Recognition using Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2309.10144/) (2023)
- [Unmasking the Uniqueness: A Glimpse into Age-Invariant Face Recognition of Indigenous African Faces](https://mlsanity.com/paper/2408.06806/) (2024)

## Abstract (arXiv)

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.