Face Frontalization Using Vision Transformers Görsel Dönüştürücüler Kullanilarak Yüz Önleştirme
34th Signal Processing and Communications Applications Conference, SIU 2026, İstanbul, Türkiye, 7 - 10 Temmuz 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/siu71813.2026.11636507
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: Face Frontalization, Self-attention Mechanism, Transformer Architecture
- Eskişehir Osmangazi Üniversitesi Adresli: Evet
Özet
This study proposes a novel Transformer-based framework for synthesizing high-quality frontal face images from profile views. Unlike conventional methods, the proposed approach FaceFrontViT leverages the self-attention mechanism to adaptively focus on sparse facial regions - such as the eyes, nose, and mouth - effectively modeling pose variations and long- range pixel dependencies based on structural symmetry. Within the framework, input images are transformed into patch-based representations incorporating linear embedding and positional encoding, which are subsequently processed through a Transformer encoder architecture. The encoder outputs are further refined by a convolutional reconstruction module to generate the final frontal images. Experimental results on Honda/UCSD recognition datasets demonstrate that our methodology outperforms existing GAN-based approaches and consistently produces high-fidelity frontal results.