Low-Resolution Face Recognition with Single Sample per Person via Domain Adaptation

Abstract

Numerous low-resolution (LR) face images are captured by a growing number of surveillance cameras nowadays. In some particular applications, such as suspect identification, it is required to recognize an LR face image captured by the surveillance camera using only one high-resolution (HR) profile face image on the ID card. This leads to LR face recognition with single sample per person (SSPP), which is more challenging than conventional LR face recognition or SSPP face recognition. To address this tough problem, we propose a Boosted Coupled Marginal Fisher Analysis (CMFA) approach, which unites domain adaptation and coupled mappings. An auxiliary database containing multiple HR and LR samples is introduced to explore more discriminative information, and locality preserving domain adaption (LPDA) is designed to realize good domain adaptation between SSPP training set (target domain) and auxiliary database (source domain). We perform LPDA on HR and LR images in both domains, then in the domain adaptation space we apply CMFA to learn the discriminative coupled mappings for classification. The learned coupled mappings embed knowledge from the auxiliary dataset, thus their discriminative ability is superior. We extensively evaluate the proposed method on FERET, LFW and SCface database, the promising results demonstrate its effectiveness on LR face recognition with SSPP.