  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.
