Work

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

Year: 2025

Type: conference-paper

Abstract: 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.

DOI: 10.1117/12.3070305

Authors: Noam Bergman, Ihsan Ozan Yildirim, Asaf Behzat Sahin, Hakan Altan, Yitzhak Yitzhaky

Institutions: Ben-Gurion University of the Negev, Ankara Yıldırım Beyazıt University, Middle East Technical University

Language: English

Topic: Face recognition and analysis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4415599980

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