Aprile 21, 2021

VMAGE Age Estimation Dataset

VGG-Face2 Mivia Age Estimation (VMAGE) Dataset

The VMAGE dataset is composed by images collected from the original VGGFace2, which is so far the largest face dataset in the world including more than 3.3 millions face images, with an average of about 362 samples per subject (minimum 87 images per subject). It also includes gender labels and consists of 62% males and 38% females and ethnicity labels.
In this page we provide age annotation for this dataset obtained automatically with a procedure described in this paper.

Age estimation from face images can be profitably employed in several applications, ranging from digital signage to social robotics, from business intelligence to access control. Only in recent years, the advent of deep learning allowed for the design of extremely accurate methods based on convolutional neural networks (CNNs) that achieve a remarkable performance in various face analysis tasks. However, these networks are not always applicable in real scenarios, due to both time and resource constraints that the most accurate approaches often do not meet. Moreover, in case of age estimation, there is the lack of a large and reliably annotated dataset for training deep neural networks.

Within this context, we propose in this paper an effective training procedure of CNNs for age estimation based on knowledge distillation, able to allow smaller and simpler “student” models to be trained to match the predictions of a larger “teacher” model. We experimentally show that such student models are able to almost reach the performance of the teacher, obtaining high accuracy over the LFW+, LAP 2016 and Adience datasets, but being up to 15 times faster. Furthermore, we evaluate the performance of the student models in presence of image corruptions, and we demonstrate that some of them are even more resilient to these corruptions than the teacher model.

1) Download the VGG-Face2 dataset here: http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/
2) You find the age annotation here: https://github.com/MiviaLab/AgeEstimationFramework/
This repository also contains trained models and code.


If you use these dataset please cite:

  • Greco, A., Saggese, A., Vento, M. et al. Effective training of convolutional neural networks for age estimation based on knowledge distillation. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-021-05981-0

For more details about the research in the field of age estimation, you can refer to:

  • V. Carletti, A. Greco, G. Percannella and M. Vento, “Age from Faces in the Deep Learning Revolution,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 9, pp. 2113-2132, 1 Sept. 2020, doi: 10.1109/TPAMI.2019.2910522.


In order to download the datasets click here.


If you have any problems, do not hesitate to contact us here.