Name
Abstract | ||
|---|---|---|
The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular biometric is expected to require less subject cooperation while permitting a larger depth of field compared to traditional ocular biometric traits (viz., iris, retina, and sclera). In this work, we study the feasibility of using the periocular region as a biometric trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set for representing and matching this region. A number of aspects are studied in this work, including the 1) effectiveness of incorporating the eyebrows, 2) use of side information (left or right) in matching, 3) manual versus automatic segmentation schemes, 4) local versus global feature extraction schemes, 5) fusion of face and periocular biometrics, 6) use of the periocular biometric in partially occluded face images, 7) effect of disguising the eyebrows, 8) effect of pose variation and occlusion, 9) effect of masking the iris and eye region, and 10) effect of template aging on matching performance. Experimental results show a rank-one recognition accuracy of 87.32% using 1136 probe and 1136 gallery periocular images taken from 568 different subjects (2 images/subject) in the Face Recognition Grand Challenge (version 2.0) database with the fusion of three different matchers. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TIFS.2010.2096810 | IEEE Transactions on Information Forensics and Security |
Keywords | Field | DocType |
eye,fusion,periocular recognition,image representation,scale invariant feature transform,image matching,visible spectra,periocular biometric,image fusion,face recognition,segmentation schemes,traditional ocular biometric trait,biometric trait,term periocular,biometrics,image segmentation,periocular biometrics,periocular region,eye region,visible spectrum,face,biometrics (access control),vicinity,feature extraction,gradient orientation histogram,facial region,information extraction,local binary patterns,different matchers,texture operators,gallery periocular image,eyebrows,image texture,point operators,iris recognition,sensors | Iris recognition,Facial recognition system,Computer vision,Pattern recognition,Image texture,Computer science,Local binary patterns,Feature extraction,Face Recognition Grand Challenge,Artificial intelligence,Biometrics,Periocular Region | Journal |
Volume | Issue | ISSN |
6 | 1 | 1556-6013 |
Citations | PageRank | References |
101 | 2.97 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Unsang Park | 1 | 815 | 36.32 |
| R. R. Jillela | 2 | 101 | 2.97 |
| A. Ross | 3 | 1246 | 64.96 |
| Anil Jain | 4 | 33507 | 3334.84 |