Video-Based Face Alignment With Local Motion Modeling

Belmonte, Romain; Ihaddadene, Nacim; Tirilly, Pierre; Bilasco, Ioan Marius; Djeraba, Chabane

doi:10.1109/wacv.2019.00228

Cited by 6 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, robust landmark detection techniques have been developed that work in-the-wild conditions, on video sequences with variations in pose, illumination, and with occlusion conditions [ 12 ]. They use a combination of local and global motion modeling to locate and track features.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection on 2D and 3D Geometric Features to Improve Facial Expression Recognition

Perez-Gomez

Ríos-Figueroa

Rechy-Ramirez

et al. 2020

Sensors

View full text Add to dashboard Cite

An essential aspect in the interaction between people and computers is the recognition of facial expressions. A key issue in this process is to select relevant features to classify facial expressions accurately. This study examines the selection of optimal geometric features to classify six basic facial expressions: happiness, sadness, surprise, fear, anger, and disgust. Inspired by the Facial Action Coding System (FACS) and the Moving Picture Experts Group 4th standard (MPEG-4), an initial set of 89 features was proposed. These features are normalized distances and angles in 2D and 3D computed from 22 facial landmarks. To select a minimum set of features with the maximum classification accuracy, two selection methods and four classifiers were tested. The first selection method, principal component analysis (PCA), obtained 39 features. The second selection method, a genetic algorithm (GA), obtained 47 features. The experiments ran on the Bosphorus and UIVBFED data sets with 86.62% and 93.92% median accuracy, respectively. Our main finding is that the reduced feature set obtained by the GA is the smallest in comparison with other methods of comparable accuracy. This has implications in reducing the time of recognition.

show abstract

Section: Introductionmentioning

confidence: 99%