The task of facial landmark extraction is fundamental in several applications which involve facial analysis, such as facial expression analysis, identity and face recognition, facial animation, and 3D face reconstruction. Taking into account the most recent advances resulting from deep-learning techniques, the performance of methods for facial landmark extraction have been substantially improved, even on in-the-wild datasets. Thus, this article presents an updated survey on facial landmark extraction on 2D images and video, focusing on methods that make use of deep-learning techniques. An analysis of many approaches comparing the performances is provided. In summary, an analysis of common datasets, challenges, and future research directions are provided.

A review of facial landmark extraction in 2D images and videos using deep learning / M. Bodini. - In: BIG DATA AND COGNITIVE COMPUTING. - ISSN 2504-2289. - 3:1(2019), pp. 14.1-14.14. [10.3390/bdcc3010014]

A review of facial landmark extraction in 2D images and videos using deep learning

M. Bodini
2019

Abstract

The task of facial landmark extraction is fundamental in several applications which involve facial analysis, such as facial expression analysis, identity and face recognition, facial animation, and 3D face reconstruction. Taking into account the most recent advances resulting from deep-learning techniques, the performance of methods for facial landmark extraction have been substantially improved, even on in-the-wild datasets. Thus, this article presents an updated survey on facial landmark extraction on 2D images and video, focusing on methods that make use of deep-learning techniques. An analysis of many approaches comparing the performances is provided. In summary, an analysis of common datasets, challenges, and future research directions are provided.
Deep learning; Facial landmark extraction
Settore INF/01 - Informatica
Article (author)
File in questo prodotto:
File Dimensione Formato  
BDCC-03-00014.pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 2.32 MB
Formato Adobe PDF
2.32 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/872365
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 21
  • ???jsp.display-item.citation.isi??? 16
social impact