TCN: Transferable Coupled Network for Cross-Resolution Face Recognition*

Abstract

Cross-resolution face recognition (CRFR) aims to learn the matching of a low-resolution (LR) probe image with a database of high-resolution (HR) gallery images. Existing methods including super resolution and projection-based algorithms are not recognition-oriented and computationally expensive, or ignore the inter-class associations across resolutions. To address the issues, we propose a novel end-to-end Transferable Coupled Network (TCN) for CRFR. Specifically, the TCN consists of two networks for the HR and LR domains, respectively. To reduce the resolution mismatch, a transferrable triple loss (TTL) is introduced to pull together cross-resolution positive pairs (intra-class) and also enforce margins towards negative ones (inter-class) from both domains. Besides, to keep stability and faster convergence, a novel online triplet selection method is proposed. Empirically, the proposed TCN model consistently outperforms the state-of-the-art methods among various low resolutions and architectures on public LFW and SCFace benchmarks.