Coarse to Fine Training for Low-Resolution Heterogeneous Face Recognition

Recently, near-infrared (NIR) images are being increasingly used for recognizing facial images across illumination variations and in low-light conditions. In surveillance scenarios, the captured NIR may have low-resolution which results in significant loss of discriminative information along with uncontrolled pose. In this work, we address the challenging task of matching these low-resolution (LR) uncontrolled NIR images with high-resolution (HR) controlled visible (VIS) images usually present in the database. Since the probe and gallery images differ significantly in terms of pose, resolution and spectral properties, we employ a two-stage approach. First, the images are transformed into a common space using metric learning such that the images of the same subject are pushed closer and those of different subjects are pushed apart. We then define an objective function which can simultaneously push both LR NIR and HR VIS samples towards the centroids of the HR VIS samples. We show that the approach is general and can be used for other data like RGB-D and also for matching across pose. Extensive experiments conducted on five datasets shows the effectiveness of our approach. © 2018 IEEE