Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research.
@article{arxiv.1409.5114,
title = {A Survey on Heterogeneous Face Recognition: Sketch, Infra-red, 3D and Low-resolution},
author = {Shuxin Ouyang and Timothy Hospedales and Yi-Zhe Song and Xueming Li},
journal= {arXiv preprint arXiv:1409.5114},
year = {2014}
}