  Modern-day surveillance systems perform person recognition using deep
learning-based face verification networks. Most state-of-the-art facial
verification systems are trained using visible spectrum images. But, acquiring
images in the visible spectrum is impractical in scenarios of low-light and
nighttime conditions, and often images are captured in an alternate domain such
as the thermal infrared domain. Facial verification in thermal images is often
performed after retrieving the corresponding visible domain images. This is a
well-established problem often known as the Thermal-to-Visible (T2V) image
translation. In this paper, we propose a Denoising Diffusion Probabilistic
Model (DDPM) based solution for T2V translation specifically for facial images.
During training, the model learns the conditional distribution of visible
facial images given their corresponding thermal image through the diffusion
process. During inference, the visible domain image is obtained by starting
from Gaussian noise and performing denoising repeatedly. The existing inference
process for DDPMs is stochastic and time-consuming. Hence, we propose a novel
inference strategy for speeding up the inference time of DDPMs, specifically
for the problem of T2V image translation. We achieve the state-of-the-art
results on multiple datasets. The code and pretrained models are publically
available at http://github.com/Nithin-GK/T2V-DDPM
