Name
Abstract | ||
|---|---|---|
This paper suggests a facial-expression recognition in accordance with face video sequences based on a newly low-dimensional feature space proposed. Indeed, we extract a Pyramid of uniform Temporal Local Binary Pattern representation, using only XT and YT orthogonal planes (PTLBP u2). Then, a Wrapper method is applied to select the most discriminating sub-regions, and therefore, reduce the feature space that is going to be projected on a low-dimensional feature space by applying the Principal Component Analysis (PCA). Support Vector Machine (SVM) and C4.5 algorithm have been tested for the classification of facial expressions. Experiments conducted on CK + and MMI, which are the two famous facial-expression databases, have shown the effectiveness of the approach proposed under a lab-controlled environment with more than 97% of recognition rate as well as under an uncontrolled environment with more than 92%. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11042-017-5354-x | Multimedia Tools Appl. |
Keywords | Field | DocType |
Facial-expression recognition, Pyramid of uniform Temporal Local Binary Pattern (PTLBPu2), Principal Component Analysis (PCA), Discriminating sub-regions, Low-dimensional feature space | Computer vision,Feature vector,Pattern recognition,Computer science,Feature (computer vision),Local binary patterns,Support vector machine,Feature extraction,Facial expression,Feature (machine learning),Artificial intelligence,Principal component analysis | Journal |
Volume | Issue | ISSN |
77 | 15 | 1380-7501 |
Citations | PageRank | References |
0 | 0.34 | 31 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taoufik Ben Abdallah | 1 | 0 | 1.69 |
| Radhouane Guermazi | 2 | 23 | 5.55 |
| Mohamed Hammami | 3 | 181 | 30.54 |