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Conference papers

A survey of deep facial landmark detection

Yongzhe Yan 1 Xavier Naturel 2 Thierry Chateau 1 Stefan Duffner 3 Christophe Garcia 3 Christophe Blanc 1
1 COMSEE - COMputers that SEE
ISPR - Image, système de perception, robotique
3 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Facial landmark detection plays a very important role in many facial analysis applications such as identity recognition, facial expression analysis, facial animation, 3D face reconstruction as well as facial beautification. With the recent advance of deep learning, the performance of facial landmark detection, including on unconstrained in-the-wild datasets, has seen considerable improvement. This paper presents a survey of deep facial landmark detection for 2D images and video. A comparative analysis of different face alignment approaches is provided as well as some future research directions.
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Submitted on : Tuesday, July 7, 2020 - 11:43:09 AM
Last modification on : Thursday, September 9, 2021 - 2:36:02 PM
Long-term archiving on: : Friday, November 27, 2020 - 1:08:34 PM


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  • HAL Id : hal-02892002, version 1


Yongzhe Yan, Xavier Naturel, Thierry Chateau, Stefan Duffner, Christophe Garcia, et al.. A survey of deep facial landmark detection. RFIAP, Jun 2018, Paris, France. ⟨hal-02892002⟩



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