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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

Real-Time Face Features Localization with Recurrent Refined Dense CNN Architectures

verfasst von : Nicolas Livet

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

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Abstract

Based on an innovative, efficient recurrent deep learning architecture, we present a highly stable and robust technique to localize face features on still images, captured and live video sequences. This dense (Fully Convolutional) CNN architecture, referred as the Refined Dense Mobilenet (RDM), is composed of (1) a main encoder-decoder block which aims to approximate face feature locations and, (2) a sequence of refiners which aims to robustly converge at the vicinity of the features. On video sequences, architecture is adapted into a Recurrent RDM where a shape prior component is re-injected in the form of temporal heatmaps obtained at previous frame inference.
Accuracy and stability of RDM/R-RDM architectures are compared with state-of-the-art Random Forest and CNN based approaches. The idea of combining a holistic feature localizer – taking advantage of large receptive fields to minimize large error – and refiners – working at higher resolution to converge at feature vicinities – is proving high accuracy in localizing face features. We demonstrate RDM/R-RDM architectures improve localization scores on 300W and AFLW datasets. Moreover, by relying on modern, efficient convolutional blocks and based on our recurrent architecture, we deliver the first stable and accurate real-time implementation of face feature localization on low-end Mobile devices.

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Vorheriges Kapitel Pupil Center Localization Using SOMA and CNN
Nächstes Kapitel Estimation of the Distance Between Fingertips Using Silhouette and Texture Information of Dorsal of Hand
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Metadaten
Titel
Real-Time Face Features Localization with Recurrent Refined Dense CNN Architectures
verfasst von
Nicolas Livet
Copyright-Jahr
2019
Buch
Advances in Visual Computing

Print ISBN: 978-3-030-33719-3

Electronic ISBN: 978-3-030-33720-9

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-33720-9_35

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