Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Pattern Analysis and Machine Intelligence Année : 2019

Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition

Résumé

The detection and tracking of human landmarks in video streams has gained in reliability partly due to the availability of affordable RGB-D sensors. The analysis of such time-varying geometric data is playing an important role in the automatic human behavior understanding. However, suitable shape representations as well as their temporal evolution, termed trajectories, often lie to nonlinear manifolds. This puts an additional constraint (i.e., nonlinearity) in using conventional Machine Learning techniques. As a solution, this paper accommodates the well-known Sparse Coding and Dictionary Learning approach to study time-varying shapes on the Kendall shape spaces of 2D and 3D landmarks. We illustrate effective coding of 3D skeletal sequences for action recognition and 2D facial landmark sequences for macro-and micro-expression recognition. To overcome the inherent nonlinearity of the shape spaces, intrinsic and extrinsic solutions were explored. As main results, shape trajectories give rise to more discriminative time-series with suitable computational properties, including sparsity and vector space structure. Extensive experiments conducted on commonly-used datasets demonstrate the competitiveness of the proposed approaches with respect to state-of-the-art.
Fichier principal
Vignette du fichier
Preprint_Amor_TPAMIISI_2019.pdf (2.31 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02398951 , version 1 (08-12-2019)

Identifiants

Citer

Amor Ben Tanfous, Hassen Drira, Boulbaba Ben Amor. Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, pp.1-1. ⟨10.1109/TPAMI.2019.2932979⟩. ⟨hal-02398951⟩
58 Consultations
77 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More