Micro and macro facial expression recognition using advanced Local Motion Patterns

Allaert, Benjamin; Bilasco, Ioan Marius; Djeraba, Chaabane

Document type :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1109/TAFFC.2019.2949559

Title :

Micro and macro facial expression recognition using advanced Local Motion Patterns

Author(s) :

Allaert, Benjamin [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Bilasco, Ioan Marius [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
FOX MIIRE [LIFL]
Université de Lille
Djeraba, Chaabane [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Journal title :

IEEE Transactions on Affective Computing

Publisher :

Institute of Electrical and Electronics Engineers

Publication date :

2019-10-25

ISSN :

1949-3045

English keyword(s) :

Macro expression
Micro expression
Optical flow
Facial expression
Local motion patterns

HAL domain(s) :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]

English abstract : [en]

In this paper, we develop a new method that recognizes facial expressions, on the basis of an innovative Local Motion Patterns (LMP) feature. The LMP feature analyzes locally the motion distribution in order to separate ...
Show more >In this paper, we develop a new method that recognizes facial expressions, on the basis of an innovative Local Motion Patterns (LMP) feature. The LMP feature analyzes locally the motion distribution in order to separate consistent mouvement patterns from noise. Indeed, facial motion extracted from the face is generally noisy and without specific processing, it can hardly cope with expression recognition requirements especially for micro-expressions. Direction and magnitude statistical profiles are jointly analyzed in order to filter out noise. This work presents three main contributions. The first one is the analysis of the face skin temporal elasticity and face deformations during expression. The second one is a unified approach for both macro and micro expression recognition leading the way to supporting a wide range of expression intensities. The third one is the step forward towards in-the-wild expression recognition, dealing with challenges such as various intensity and various expression activation patterns, illumination variations and small head pose variations. Our method outperforms state-of-the-art methods for micro expression recognition and positions itself among top-ranked state-of-the-art methods for macro expression recognition.Show less >

Language :

Anglais

Popular science :

Non

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