# Improving Face Recognition with Large Age Gaps by Learning to Distinguish Children

arXiv: https://arxiv.org/abs/2110.11630 · HTML: https://mlsanity.com/paper/2110.11630/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition](https://mlsanity.com/paper/2004.00288/) (2020)
- [Mis-classified Vector Guided Softmax Loss for Face Recognition](https://mlsanity.com/paper/1912.00833/) (2019)
- [BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition](https://mlsanity.com/paper/2008.06674/) (2020)
- [When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework](https://mlsanity.com/paper/2103.01520/) (2021)
- [Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Are](https://mlsanity.com/paper/2008.06989/) (2020)
- [Decorrelated Adversarial Learning for Age-Invariant Face Recognition](https://mlsanity.com/paper/1904.04972/) (2019)
- [Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition](https://mlsanity.com/paper/1809.00338/) (2018)
- [Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition](https://mlsanity.com/paper/1810.07599/) (2018)
- [Large age-gap face verification by feature injection in deep networks](https://mlsanity.com/paper/1602.06149/) (2016)

## Built on by (most cited first, 5 of 5)

- [CAFA: Class-Aware Feature Alignment for Test-Time Adaptation](https://mlsanity.com/paper/2206.00205/) (2022)
- [Label Shift Adapter for Test-Time Adaptation under Covariate and Label Shifts](https://mlsanity.com/paper/2308.08810/) (2023)
- [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)
- [Improving Scene Text Recognition for Character-Level Long-Tailed Distribution](https://mlsanity.com/paper/2304.08592/) (2023)
- [Age-Invariant Face Embedding using the Wasserstein Distance](https://mlsanity.com/paper/2305.02745/) (2023)

## Abstract (arXiv)

Despite the unprecedented improvement of face recognition, existing face recognition models still show considerably low performances in determining whether a pair of child and adult images belong to the same identity. Previous approaches mainly focused on increasing the similarity between child and adult images of a given identity to overcome the discrepancy of facial appearances due to aging. However, we observe that reducing the similarity between child images of different identities is crucial for learning distinct features among children and thus improving face recognition performance in child-adult pairs. Based on this intuition, we propose a novel loss function called the Inter-Prototype loss which minimizes the similarity between child images. Unlike the previous studies, the Inter-Prototype loss does not require additional child images or training additional learnable parameters. Our extensive experiments and in-depth analyses show that our approach outperforms existing baselines in face recognition with child-adult pairs. Our code and newly-constructed test sets of child-adult pairs are available at https://github.com/leebebeto/Inter-Prototype.