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• W1578442502 abstract "Facial feature extraction plays an important role in many multimedia and vision applications. Recent regression methods for extraction lack the explicit shape constraints for faces, and require a large number of facial images covering great appearance variations. This paper introduces a novel projective invariant, named characteristic number (CN), to explicitly characterize the intrinsic geometries of facial points shared by human faces, which is inherently invariant to pose changes. By further developing a shape-to-gradient regression framework, we provide a robust and efficient feature extractor for facial images in the wild. The computation of our model can be successfully addressed by learning the descent directions using point-CN pairs without the need of large collections for appearance training. Extensive experiments on challenging benchmark data sets demonstrate the effectiveness of our proposed detector against other state-of-the-art approaches." @default.
• W1578442502 created "2016-06-24" @default.
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• W1578442502 date "2015-06-01" @default.
• W1578442502 modified "2023-09-26" @default.
• W1578442502 title "Characteristic number regression for facial feature extraction" @default.
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• W1578442502 doi "https://doi.org/10.1109/icme.2015.7177496" @default.
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