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• W3206121521 abstract "Even the most impressive achievement in frontal face synthesis is challenged by large poses and low-quality data given one single side-view face. We propose a synthesizer called SuperFront GAN (SF-GAN) to accept one or more low-resolution (LR) faces at the input to then output a high-resolution (HR) frontal face with various poses and such to preserve identity information. SF-GAN includes intra-class and inter-class constraints, which allow it to learn an identity-preserving representation from multiple LR faces in an improved, comprehensive manner. We adopt an orthogonal loss as the intra-class constraint that diversifies the learned feature-space per subject. Hence, each sample is made to complement the others to its max ability. Additionally, a triplet loss is used as the inter-class constraint: it improves the discriminative power of the new representation, which, hence, maintains the identity information. Furthermore, we integrate a super-resolution (SR) side-view module as part of the SF-GAN to help preserve the finer details of HR side-views. This helps the model reconstruct the high-frequency parts of the face (i.e. periocular region, nose, and mouth regions). Quantitative and qualitative results demonstrate the superiority of SF-GAN. SF-GAN holds promise as a pre-processing step to normalize and align faces before passing to CV system for processing." @default.
• W3206121521 created "2021-10-25" @default.
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• W3206121521 date "2021-10-17" @default.
• W3206121521 modified "2023-09-25" @default.
• W3206121521 title "SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis" @default.
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• W3206121521 doi "https://doi.org/10.1145/3474085.3475300" @default.
• W3206121521 hasPublicationYear "2021" @default.
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