Large age-gap face verification by feature injection in deep networks.

A new large-scale Large Age Gap dataset is collected, that includes 1010 identities.A new Deep Convolutional Neural Network (DCNN) architecture is proposed.The proposed DCNN includes a feature injection layer that performs feature fusion.The proposed DCNN outperforms state-of-the-art methods on LAG dataset. 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. Fine-tuning 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.