Face image quality is quantified as the Euclidean distance between embeddings from a pre-trained face recognition model and its pruned version, achieving competitive or superior results without training or supervision.
Biometric quality: Re- view and application to face recognition with face- qnet
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Fusing quality scores from multiple intermediate transformer blocks in ViTs via depth-weighted averaging improves face image quality assessment on benchmarks without retraining or architecture changes.
ATTN-FIQA computes face image quality scores from pre-softmax attention patterns in pre-trained ViT-based FR models using a single forward pass, showing correlation with recognition utility and spatial interpretability.
citing papers explorer
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PreFIQs: Face Image Quality Is What Survives Pruning
Face image quality is quantified as the Euclidean distance between embeddings from a pre-trained face recognition model and its pruned version, achieving competitive or superior results without training or supervision.
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EX-FIQA: Leveraging Intermediate Early eXit Representations from Vision Transformers for Face Image Quality Assessment
Fusing quality scores from multiple intermediate transformer blocks in ViTs via depth-weighted averaging improves face image quality assessment on benchmarks without retraining or architecture changes.
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ATTN-FIQA: Interpretable Attention-based Face Image Quality Assessment with Vision Transformers
ATTN-FIQA computes face image quality scores from pre-softmax attention patterns in pre-trained ViT-based FR models using a single forward pass, showing correlation with recognition utility and spatial interpretability.