Name
Title | ||
|---|---|---|
Application of Single Image Super-Resolution in Human Ear Recognition Using Eigenvalues |
Abstract | ||
|---|---|---|
Ear recognition is a field in biometrics wherein images of the ears are used to identify individuals. Many techniques have been developed for ear recognition; however, most of the existing techniques have been tested on high-resolution images taken in a laboratory environment. This research examines the performance of Principal Component Analysis (PCA) based ear recognition in conjunction with super-resolution algorithms from low-resolution ear images. Ear images are first split into database and query images; the latter are first filtered and down-sampled, generating a set ear images of different low resolutions. The resulting low-resolution images are then enlarged to their original sizes using an assortment of neural network-based and statistical-based super-resolution methods. PCA is then applied to the images, generating their eigenvalues, which are used as features for matching. Experimental results on the images of a benchmark dataset show that the statistical-based super-resolution techniques, namely those that are wavelet-based, outperform other algorithms with respect to ear recognition accuracy. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IST.2018.8577134 | 2018 IEEE International Conference on Imaging Systems and Techniques (IST) |
Keywords | Field | DocType |
ear recognition,super-resolution,principal component analysis,eigenvalues | Ear recognition,Computer vision,Pattern recognition,Computer science,Artificial intelligence,Biometrics,Artificial neural network,Superresolution,Principal component analysis,Eigenvalues and eigenvectors | Conference |
ISSN | ISBN | Citations |
1558-2809 | 978-1-5386-6629-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Zarachoff | 1 | 0 | 1.35 |
| Akbar Sheikh Akbari | 2 | 0 | 1.69 |
| Dorothy Ndedi Monekosso | 3 | 61 | 11.89 |