A deep-learning framework for concealed and unconcealed face analysis in sub millimeter wave imaging

This work presents a deep learning framework for sub-millimeter-wave (Sub-MMW) facial analysis that operates on active 340 GHz intensity images acquired from 20 identities in both concealed and unconcealed conditions over five head postures, including front-facing and yawed views, with approximately 35 images per condition per identity (~1,400 total images). The framework addresses concealed-to-unconcealed face verification, head posture classification, and pixel-wise reconstruction of unconcealed from concealed observations, showing that task-tailored networks achieve strong performance mainly in verification, and posture recognition, while reproducing and extending a structural similarity (SSIM) sliding-window baseline for concealed–unconcealed verification reported for the same instrument class.