  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.
