ViTNT-FIQA: Training-Free Face Image Quality Assessment with Vision Transformers
Face Image Quality Assessment (FIQA) is essential for reliable face recognition systems. Current approaches primarily exploit only final-layer representations, while training-free methods require multiple forward passes or backpropagation. We propose ViTNT-FIQA, a training-free approach that measures the stability of patch embedding evolution across intermediate Vision Transformer (ViT) blocks. We demonstrate that high-quality face images exhibit stable feature refinement trajectories across blocks, while degraded images show erratic transformations. Our method computes Euclidean distances between L2-normalized patch embeddings from consecutive transformer blocks and aggregates them into image-level quality scores. We empirically validate this correlation on a quality-labeled synthetic dataset with controlled degradation levels. Unlike existing training-free approaches, ViTNT-FIQA requires only a single forward pass without backpropagation or architectural modifications. Through extensive evaluation on eight benchmarks (LFW, AgeDB-30, CFP-FP, CALFW, Adience, CPLFW, XQLFW, IJB-C), we show that ViTNT-FIQA achieves competitive performance with state-of-the-art methods while maintaining computational efficiency and immediate applicability to any pre-trained ViT-based face recognition model.
6964d033138cc47cbd765476
Guray Ozgur
claimed_verified
2026-01-12T11:08:27.423Z
63a5eeff64f4700278176929
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Guray Ozgur
gurayozgur
user
gurayozgur
6964d033138cc47cbd765477
Eduarda Caldeira
6964d033138cc47cbd765478
Tahar Chettaoui
6964d033138cc47cbd765479
Jan Niklas Kolf
6964d033138cc47cbd76547a
Marco Huber
6964d033138cc47cbd76547b
Naser Damer
6964d033138cc47cbd76547c
Fadi Boutros
ViTNT-FIQA: Training-Free Face Image Quality Assessment with Vision Transformers
Face Image Quality Assessment (FIQA) is essential for reliable face recognition systems. Current approaches primarily exploit only final-layer representations, while training-free methods require multiple forward passes or backpropagation. We propose ViTNT-FIQA, a training-free approach that measures the stability of patch embedding evolution across intermediate Vision Transformer (ViT) blocks. We demonstrate that high-quality face images exhibit stable feature refinement trajectories across blocks, while degraded images show erratic transformations. Our method computes Euclidean distances between L2-normalized patch embeddings from consecutive transformer blocks and aggregates them into image-level quality scores. We empirically validate this correlation on a quality-labeled synthetic dataset with controlled degradation levels. Unlike existing training-free approaches, ViTNT-FIQA requires only a single forward pass without backpropagation or architectural modifications. Through extensive evaluation on eight benchmarks (LFW, AgeDB-30, CFP-FP, CALFW, Adience, CPLFW, XQLFW, IJB-C), we show that ViTNT-FIQA achieves competitive performance with state-of-the-art methods while maintaining computational efficiency and immediate applicability to any pre-trained ViT-based face recognition model.
ViTNT-FIQA measures face image quality by analyzing patch embedding stability across Vision Transformer blocks with a single forward pass.
Vision Transformer patch embedding Euclidean distance L2-normalized transformer blocks face image quality assessment training-free approach feature refinement trajectory