Name
Abstract | ||
|---|---|---|
People often make instant judgments about the age, health, mood, personality and character of others based on their facial features. It is not clear from a cognitive aspect whether these different traits require different sets of features or a shared feature set. Till date, much of the computational face image analysis work such as face recognition, face-based deceit detection, age estimation, gender estimation, etc, have been developed on datasets and features specific only to the problem-at-hand. In this paper, we explore an approach for performing face image analysis using a shared set of features for different tasks. By performing unsupervised learning on a large collection of face images, we learn the parameters of a probabilistic generative face model, and by projecting a new face image into this probabilistic space, we obtain a set of face features not created for any specific face analysis tasks. We investigate the use of such shared features and successfully predict the level of attractiveness, whether or not a face is made-up, the facial expression, and the gender of a person, given any arbitrary, near-frontal face image. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SMC.2014.6973943 | Systems, Man and Cybernetics |
Keywords | Field | DocType |
face recognition,probability,unsupervised learning,age estimation,computational face image analysis,face image analysis,face recognition,face-based biometrics,face-based deceit detection,facial features,gender estimation,multiple face-based biometrics,shared features,unsupervised learning | Computer vision,Object-class detection,Three-dimensional face recognition,Computer science,Speech recognition,Artificial intelligence,Biometrics,Face detection | Conference |
ISSN | Citations | PageRank |
1062-922X | 1 | 0.35 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ifeoma Nwogu | 1 | 86 | 13.70 |
| Yingbo Zhou | 2 | 263 | 19.43 |