Name
Abstract | ||
|---|---|---|
A major drawback of statistical models of non-rigid, deformable objects, such as the active appearance model (AAM), is the required pseudo-dense annotation of landmark points for every training image. We propose a regression-based approach for automatic annotation of face images at arbitrary pose and expression, and for deformable model building using only the annotated frontal images. We pose the problem of learning the pattern of manual annotation as a data-driven regression problem and explore several regression strategies to effectively predict the spatial arrangement of the landmark points for unseen face images, with arbitrary expression, at arbitrary poses. We show that the proposed fully sparse non-linear regression approach outperforms other regression strategies by effectively modelling the changes in the shape of the face under varying pose and is capable of capturing the subtleties of different facial expressions at the same time, thus, ensuring the high quality of the generated synthetic images. We show the generalisability of the proposed approach by automatically annotating the face images from four different databases and verifying the results by comparing them with a ground truth obtained from manual annotations. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.patcog.2011.03.014 | Pattern Recognition |
Keywords | Field | DocType |
face image,automatic face annotation,sparse non-linear regression approach,deformable face model,correspondence problem,automatic annotation,landmark point,active appearance model,deformable model building,arbitrary expression,regression strategy,unseen face image,data-driven regression problem,manual annotation,facial expression,ground truth,statistical model,non linear regression | Computer vision,Annotation,Pattern recognition,Computer science,Model building,Image processing,Active appearance model,Ground truth,Artificial intelligence,Statistical model,Correspondence problem,Landmark | Journal |
Volume | Issue | ISSN |
44 | 10-11 | Pattern Recognition |
Citations | PageRank | References |
7 | 0.46 | 32 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akshay Asthana | 1 | 729 | 25.02 |
| Simon Lucey | 2 | 2034 | 116.77 |
| Roland Goecke | 3 | 1323 | 69.44 |