SUPREAR-NET: Supervised Resolution Enhancement and Recognition Network
Soumyadeep Ghosh, Mayank Vatsa, Richa Singh · IEEE Transactions on Biometrics Behavior and Identity Science · 2022
Heterogeneous face recognition is a challenging problem where the probe and gallery images belong to different modalities such as, low and high resolution, visible and near-infrared spectrum. A Generative Adversarial Network (GAN) enables us to learn an image to image transformation model for enhancing the resolution of a face image. Such a model would be helpful in a heterogeneous face recognition scenario. However, unsupervised GAN based transformation methods in their native formulation might alter useful discriminative information in the transformed face images. This affects the performance of face recognition algorithms when applied on the transformed images. We propose a Supervised Resolution Enhancement and Recognition Network (SUPREAR-NET), which does not corrupt the useful class-specific information of the face image and transforms a low resolution probe image into a high resolution one, followed by effective matching with the gallery using a trained discriminative model. We show the results for cross-resolution face recognition on three datasets including the FaceSurv face dataset, containing poor quality low resolution videos captured at a standoff distance up to 10 meters from the camera. On the FaceSurv, NIST MEDS and CMU MultiPIE datasets, the proposed algorithm outperforms recent unsupervised and supervised GAN algorithms.