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Idiap Research Institute, Rue Marconi 19, 1920 Martigny
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Real-world conditions expose critical performance gaps in face recognition systems, with state-of-the-art models struggling under the challenges of motion blur and occlusion.
Resource-strapped edge devices can now achieve state-of-the-art face recognition across different sensing modalities thanks to a new lightweight CNN-Transformer architecture.
Cosine distance unexpectedly cracks PolyProtect, but a smart key selection algorithm can harden it again, offering better control over the accuracy-irreversibility tradeoff.
