Name
Abstract | ||
|---|---|---|
The assumption that the number of training samples is less than the number of pixels in a face image is essential for conventional eigenface-based face recognition. But recently, it has become impractical for massive face image collections. A parallel processing method using distributed eigenfaces is presented. A massive face image set was divided into a bunch of small subsets that satisfied the assumption of conventional approaches. Eigenfaces were extracted from the subsets and stored in a cloud system. Face recognition was performed by parallel processing using the distributed eigenfaces in the cloud system. A face recognition system was implemented in the Hadoop system. Various experiments were performed to test the validity of the distributed eigenface-based approach. The experimental results show that, compared to conventional methods, the implemented distributed face recognition system worked well for large datasets without significant performance degradation. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11042-017-4823-6 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Eigenface, Face recognition, Parallel processing, Hadoop | Computer vision,Facial recognition system,Eigenface,Cloud systems,Pattern recognition,Computer science,Parallel processing,Pixel,Artificial intelligence,Cloud computing | Journal |
Volume | Issue | ISSN |
76 | 24 | 1380-7501 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeong-Keun Park | 1 | 0 | 0.34 |
| Ho-Hyun Park | 2 | 114 | 28.18 |
| Jaehwa Park | 3 | 65 | 9.50 |