Age and Gender Prediction From Face Images Using Attentional Convolutional Network

TL;DR

This paper introduces a deep learning framework combining attentional and residual convolutional networks for accurate age and gender prediction from face images, leveraging attention mechanisms and multi-task learning.

Contribution

The work presents a novel ensemble of attentional and residual networks with multi-task training, improving prediction accuracy on face images compared to existing models.

Findings

High accuracy in age and gender prediction achieved

Attention maps focus on relevant facial regions

Multi-task learning enhances age prediction accuracy

Abstract

Automatic prediction of age and gender from face images has drawn a lot of attention recently, due it is wide applications in various facial analysis problems. However, due to the large intra-class variation of face images (such as variation in lighting, pose, scale, occlusion), the existing models are still behind the desired accuracy level, which is necessary for the use of these models in real-world applications. In this work, we propose a deep learning framework, based on the ensemble of attentional and residual convolutional networks, to predict gender and age group of facial images with high accuracy rate. Using attention mechanism enables our model to focus on the important and informative parts of the face, which can help it to make a more accurate prediction. We train our model in a multi-task learning fashion, and augment the feature embedding of the age classifier, with the predicted gender, and show that doing so can further increase the accuracy of age prediction. Our model is trained on a popular face age and gender dataset, and achieved promising results. Through visualization of the attention maps of the train model, we show that our model has learned to become sensitive to the right regions of the face.