Name
Abstract | ||
|---|---|---|
The field of periocular biometrics has gained wide attention as an alternative or supplemental means to conventional biometric traits such as the iris or the face. Periocular biometrics provide intermediate resolution between the iris and the face, which enables it to support both. We have developed a periocular recognition system by using uniform Multiscale Local Binary Pattern (uMLBP) and attribute features. The proposed system has been evaluated in terms of major factors that need to be considered on a mobile platform (e.g., distance and facial pose) to assess the feasibility of the use of periocular biometrics on mobile devices. Experimental results showed 98.7% of rank-1 identification accuracy on a subset of the Face Recognition Grand Challenge (FRGC) database, which is the best performance among similar studies. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3837/tiis.2017.12.024 | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS |
Keywords | Field | DocType |
periocular biometrics,uMLBP,periocular attribute classifiers,partial least squares,FRGC | Computer science,Artificial intelligence,Machine learning,Distributed computing | Journal |
Volume | Issue | ISSN |
11 | 12 | 1976-7277 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zahid Ali | 1 | 0 | 1.01 |
| Unsang Park | 2 | 815 | 36.32 |
| Jongho Nang | 3 | 57 | 12.54 |
| Jeong-seon Park | 4 | 202 | 18.13 |
| Taehwa Hong | 5 | 0 | 0.34 |
| Sungjoo Park | 6 | 0 | 2.70 |