Name
Abstract | ||
|---|---|---|
This paper proposes a facial expression recognition algorithm, which automatically detects and segments the face regions of interest (ROI) such as the forehead, eyes and mouth, etc. Proposed scheme initially detects the image face and segments it in two regions: forehead/eyes and mouth. Next each of these regions is segmented into N × M blocks which are characterized using 54 Gabor functions that are correlated with each one of the N × M blocks. Next the principal component analysis (PCA) is used for dimensionality reduction. Finally, the resulting feature vectors are inserted in a proposed classifier based on clustering techniques which provides recognition results closed to those provided by the support vector machine (SVM) with much less computational complexity. The experimental results show that proposed system provides a recognition rate of about 98 % when only one ROI is used. This recognition rate increases to about 99 % when the feature vectors of all ROIs are concatenated. This fact allows achieving recognition rates higher than 97 %, even when one of the two ROI are totally occluded. |
Year | Venue | Field |
|---|---|---|
2015 | SoMeT | Feature vector,Dimensionality reduction,Pattern recognition,Three-dimensional face recognition,Computer science,Segmentation,Support vector machine,Theoretical computer science,Artificial intelligence,Face detection,Cluster analysis,Principal component analysis |
DocType | Citations | PageRank |
Conference | 1 | 0.38 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andres Hernandez-Matamoros | 1 | 1 | 0.38 |
| Andrea Bonarini | 2 | 623 | 76.73 |
| Enrique Escamilla Hernández | 3 | 30 | 4.97 |
| Mariko Nakano-Miyatake | 4 | 144 | 29.99 |
| Héctor Pérez-Meana | 5 | 48 | 15.66 |