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.
Large Age-Gap (LAG) database
Examples of face crops for matching pairs in the Large Age-Gap (LAG) dataset
The dataset contains 3,828 images of 1,010 celebrities. For each identity at least one child/young image and one adult/old image are present.
LAG database (100×100) (.zip, 70MB)
LAG database (200×200) (.zip, 215MB)
If you use these data, please cite:
@article{bianco2017large-age,
author = {Bianco, Simone},
year = {2017},
pages = {36-42},
title = {Large Age-Gap Face Verification by Feature Injection in Deep Networks},
volume = {90},
journal = {Pattern Recognition Letters},
doi = {10.1016/j.patrec.2017.03.006}}
Publications
1.
Large Age-Gap Face Verification by Feature Injection in Deep Networks (Simone Bianco)
In Pattern Recognition Letters, volume 90, pp. 36-42, 2017.
@article{bianco2017large-age, author = {Bianco, Simone}, year = {2017}, pages = {36-42}, title = {Large Age-Gap Face Verification by Feature Injection in Deep Networks}, volume = {90}, journal = {Pattern Recognition Letters}, pdf = {/download/bianco2017large-age.pdf}, doi = {10.1016/j.patrec.2017.03.006}, projectref = {http://www.ivl.disco.unimib.it/activities/large-age-gap-face-verification/}}
