  Thermal face image analysis is favorable for certain circumstances. For
example, illumination-sensitive applications, like nighttime surveillance; and
privacy-preserving demanded access control. However, the inadequate study on
thermal face image analysis calls for attention in responding to the industry
requirements. Detecting facial landmark points are important for many face
analysis tasks, such as face recognition, 3D face reconstruction, and face
expression recognition. In this paper, we propose a robust neural network
enabled facial landmark detection, namely Deep Multi-Spectral Learning (DMSL).
Briefly, DMSL consists of two sub-models, i.e. face boundary detection, and
landmark coordinates detection. Such an architecture demonstrates the
capability of detecting the facial landmarks on both visible and thermal
images. Particularly, the proposed DMSL model is robust in facial landmark
detection where the face is partially occluded, or facing different directions.
The experiment conducted on Eurecom's visible and thermal paired database shows
the superior performance of DMSL over the state-of-the-art for thermal facial
landmark detection. In addition to that, we have annotated a thermal face
dataset with their respective facial landmark for the purpose of
experimentation.
