Large age-gap face verification by feature injection in deep networks
This addresses the problem of identifying individuals across significant age variations, which is incremental as it builds on existing face verification methods with a specific enhancement.
The paper tackles face verification across large age gaps by fine-tuning a pre-trained deep convolutional neural network with a Siamese architecture and a novel feature injection layer, achieving state-of-the-art results on their introduced 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. 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.