Name
Abstract | ||
|---|---|---|
This paper focuses on a software framework to support face recognition, a specific area of image processing. For the processing approach, we use principal component analysis (PCA), a data dimensionality reduction approach. The goal of this study is to understand the entire face recognition process with PCA and to present a software framework supporting multiple variations, which can be used to help users create customized face recognition applications efficiently. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/SWSTE.2016.11 | 2016 IEEE International Conference on Software Science, Technology and Engineering (SWSTE) |
Keywords | Field | DocType |
PCA,Face Recognition,Software engineering | Data mining,Facial recognition system,Entire face,Computer science,Image processing,Face Recognition Grand Challenge,MULTIPLE VARIATIONS,Data dimensionality reduction,Artificial intelligence,Principal component analysis,Software framework,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5090-1019-6 | 0 | 0.34 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Peng | 1 | 19 | 12.21 |
| Paulo S. C. Alencar | 2 | 393 | 45.89 |
| Donald D. Cowan | 3 | 581 | 90.75 |