Name
Abstract | ||
|---|---|---|
Complexion plays a remarkably important role in recognition. Experiments with human subjects have shown that complexion provides as much distinctiveness as other well-known features such as the shape of the face. From the perspective an autonomous robot, changes in lighting (e.g., intensity, orientation) and camera parameters (e.g., white balance) can make capturing complexion challenging. In this paper, we evaluate complexion as a soft biometric using color (histograms) and texture (local binary patterns). We train a linear SVM to distinguish between the individual and impostors. We demonstrate the performance of this approach on a database of over 200 individuals collected to study biometrics in human-robot interaction. In our experiment, we identify 9 individuals that interact with the robot on a regular basis, rejecting all others as unknown. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/BTAS.2013.6712703 | Biometrics: Theory, Applications and Systems |
Keywords | Field | DocType |
biometrics (access control),human-robot interaction,image colour analysis,image texture,robot vision,support vector machines,color,complexion,human-robot interaction,linear SVM,soft biometric,support vector machines,texture | Computer vision,Image texture,Computer science,Local binary patterns,Feature extraction,Color balance,Artificial intelligence,Complexion,Biometrics,Autonomous robot,Human–robot interaction | Conference |
Citations | PageRank | References |
1 | 0.35 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wallace E. Lawson | 1 | 13 | 7.73 |
| J. Gregory Trafton | 2 | 52 | 6.18 |
| Eric Martinson | 3 | 124 | 12.18 |