# Large age-gap face verification by feature injection in deep networks

arXiv: https://arxiv.org/abs/1602.06149 · HTML: https://mlsanity.com/paper/1602.06149/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2016.

## Built on by (most cited first, 10 of 10)

- [Face Recognition In Children: A Longitudinal Study](https://mlsanity.com/paper/2204.01760/) (2022)
- [Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack](https://mlsanity.com/paper/2304.11436/) (2023)
- [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)
- [On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms](https://mlsanity.com/paper/2310.15848/) (2023)
- [SELM: Siamese Extreme Learning Machine with Application to Face Biometrics](https://mlsanity.com/paper/2108.03140/) (2021)
- [Applying Artificial Intelligence for Age Estimation in Digital Forensic Investigations](https://mlsanity.com/paper/2201.03045/) (2022)
- [CompNet: A Designated Model to Handle Combinations of Images and Designed features](https://mlsanity.com/paper/2209.14454/) (2022)
- [An information-theoretic learning model based on importance sampling](https://mlsanity.com/paper/2302.04438/) (2023)
- [Unmasking the Uniqueness: A Glimpse into Age-Invariant Face Recognition of Indigenous African Faces](https://mlsanity.com/paper/2408.06806/) (2024)

## Abstract (arXiv)

This paper introduces a new method for face verification across large age gaps and also a dataset containing variations of age in the wild, the Large Age-Gap (LAG) dataset, with images ranging from child/young to adult/old. The proposed method exploits a deep convolutional neural network (DCNN) pre-trained for the face recognition task on a large dataset and then fine-tuned for the large age-gap face verification task. Finetuning is performed in a Siamese architecture using a contrastive loss function. A feature injection layer is introduced to boost verification accuracy, showing the ability of the DCNN to learn a similarity metric leveraging external features. Experimental results on the LAG dataset show that our method is able to outperform the face verification solutions in the state of the art considered.