A deep-learning framework for concealed and unconcealed face analysis in submillimeter wave imaging
3rd Artificial Intelligence for Security and Defence Applications, Madrid, İspanya, 16 - 18 Eylül 2025, cilt.13679, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 13679
- Doi Numarası: 10.1117/12.3070305
- Basıldığı Şehir: Madrid
- Basıldığı Ülke: İspanya
- Anahtar Kelimeler: Concealed Face Recognition, Deep Learning, Structural Similarity Index (SSIM), Sub-millimeter wave imaging, Terahertz (THz) imaging
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
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.