Multi-spectral Facial Landmark Detection
This addresses a domain-specific problem for industries needing robust face analysis in low-light or privacy-sensitive scenarios, but it is incremental as it builds on existing neural network approaches.
The paper tackles facial landmark detection in thermal images, which is understudied but important for applications like nighttime surveillance and privacy-preserving access control, and proposes Deep Multi-Spectral Learning (DMSL) that achieves superior performance over state-of-the-art methods on a paired visible-thermal database.
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.