Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model

TL;DR

This paper demonstrates that a deep CNN trained for face recognition can be effectively repurposed for age estimation, leveraging pretraining and large datasets to improve accuracy in unconstrained face images.

Contribution

It shows that face recognition CNN models can be adapted for age estimation, highlighting the importance of pretraining and dataset size for performance.

Findings

Pretrained face recognition CNN improves age estimation accuracy.

Overfitting is mitigated by using pretrained models on large datasets.

Pretraining task influences the effectiveness of age estimation.

Abstract

Automatic age estimation from real-world and unconstrained face images is rapidly gaining importance. In our proposed work, a deep CNN model that was trained on a database for face recognition task is used to estimate the age information on the Adience database. This paper has three significant contributions in this field. (1) This work proves that a CNN model, which was trained for face recognition task, can be utilized for age estimation to improve performance; (2) Over fitting problem can be overcome by employing a pretrained CNN on a large database for face recognition task; (3) Not only the number of training images and the number subjects in a training database effect the performance of the age estimation model, but also the pre-training task of the employed CNN determines the performance of the model.