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- W4308233880 abstract "This paper demonstrates a novel approach to improve face-recognition pose-invariance using semantic-segmentation features. The proposed Seg-Distilled-ID network jointly learns identification and semantic-segmentation tasks, where the segmentation task is then distilled (MobileNet en-coder). Performance is benchmarked against three state-of-the-art encoders on a publicly available data-set emphasizing head-pose variations. Experimental evaluations show the Seg-Distilled-ID network shows notable robustness benefits, achieving 99.9% test-accuracy in comparison to 81.6% on ResNet-101, 96.1% on VGG-19 and 96.3% on InceptionV3. This is achieved using approximately one-tenth of the top encoder’s inference parameters. These results demonstrate distilling semantic-segmentation features can efficiently ad-dress face-recognition pose-invariance." @default.
- W4308233880 created "2022-11-09" @default.
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- W4308233880 date "2022-10-16" @default.
- W4308233880 modified "2023-10-01" @default.
- W4308233880 title "Distilling Facial Knowledge with Teacher-Tasks: Semantic-Segmentation-Features For Pose-Invariant Face-Recognition" @default.
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- W4308233880 doi "https://doi.org/10.1109/icip46576.2022.9897793" @default.
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