  Heterogeneous face recognition (HFR) refers to matching face images acquired
from different sources (i.e., different sensors or different wavelengths) for
identification. HFR plays an important role in both biometrics research and
industry. In spite of promising progresses achieved in recent years, HFR is
still a challenging problem due to the difficulty to represent two
heterogeneous images in a homogeneous manner. Existing HFR methods either
represent an image ignoring the spatial information, or rely on a
transformation procedure which complicates the recognition task. Considering
these problems, we propose a novel graphical representation based HFR method
(G-HFR) in this paper. Markov networks are employed to represent heterogeneous
image patches separately, which takes the spatial compatibility between
neighboring image patches into consideration. A coupled representation
similarity metric (CRSM) is designed to measure the similarity between obtained
graphical representations. Extensive experiments conducted on multiple HFR
scenarios (viewed sketch, forensic sketch, near infrared image, and thermal
infrared image) show that the proposed method outperforms state-of-the-art
methods.
