From Local Geometry to Global Structure: Learning Latent Subspace for Low-resolution Face Image Recognition

• Published in: IEEE signal processing letters Vol. 22; no. 5; pp. 554 - 558
• Main Authors: Shi, Jingang, Qi, Chun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Construction; Coupled mappings; Face; Face recognition; Geometry; Image resolution; Joining; Local optimization; low-resolution; Mapping; Object recognition; Optimization; Signal processing algorithms; subspace learning; Subspaces; Training

In this letter, we propose a novel approach for learning coupled mappings to improve the performance of low-resolution (LR) face image recognition. The coupled mappings aim to project the LR probe images and high-resolution (HR) gallery images into a unified latent subspace, which is efficient to me...