On Privileged Information Driven Robust Face Verification: A Siamese Convolutional Neural Network Approach
Ruohan Zong · 2020
Face verification is an important task to verify people's identities, which has sample pairs and labels with only side information about whether the two images in a pair are from the same subject or not instead of a specific label for each sample. This paper is motivated by the limitations of previous efforts that the deep neural networks with only the RGB feature are not robust to intra-class noise (e.g., illumination), and the deep networks with both the RGB and depth feature are not robust to data availability of the depth images in applications. To overcome these limitations, we focus on developing a "double robust" face verification architecture using the Siamese convolutional neural network (SCNN). In particular, our goal is to propose an SCNN with privileged information (SCNN+), which is inspired by Learning Using Privileged Information (LUPI), through incorporating additional depth feature along with the RGB feature in the training stage to provide privileged information to restrain the RGB feature's prediction error. We conduct experiments to evaluate the performance of the proposed SCNN+ architecture and compare it with different categories of state-of-the-art baselines on two real-world RGB-D face datasets. The evaluation results demonstrate that SCNN+ significantly outperforms all types of baselines.