Name
Abstract | ||
|---|---|---|
Periocular recognition has gained attention in the last years thanks to its high discrimination capabilities in less constraint scenarios than other ocular modalities. In this paper we propose a method for periocular verification under different light spectra using CNN features with the particularity that the network has not been trained for this purpose. We use a ResNet-101 pretrained model for the ImageNet Large Scale Visual Recognition Challenge to extract features from the IIITD Multispectral Periocular Database. At each layer the features are compared using χ
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>
distance and cosine similitude to carry on verification between images, achieving an improvement in the EER and accuracy at 1% FAR of up to 63.13% and 24.79% in comparison to previous works that employ the same database. In addition to this, we train a neural network to match the best CNN feature layer vector from each spectrum. With this procedure, we achieve improvements of up to 65% (EER) and 87% (accuracy at 1% FAR) in cross-spectral verification with respect to previous studies. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICB45273.2019.8987303 | 2019 International Conference on Biometrics (ICB) |
Keywords | Field | DocType |
EER,cross-spectral verification,ResNet features,periocular recognition,discrimination capabilities,ocular modalities,periocular verification,light spectra,CNN features,ResNet-101 pretrained model,ImageNet Large Scale Visual Recognition Challenge,cosine similitude,neural network training,CNN feature layer vector,IIITD multispectral periocular database,cross spectral periocular matching,feature extraction | Modalities,Computer vision,Signal processing,Pattern recognition,Computer science,Artificial intelligence,Residual neural network | Conference |
ISSN | ISBN | Citations |
2376-4201 | 978-1-7281-3641-7 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin Hernandez-Diaz | 1 | 5 | 1.76 |
| Fernando Alonso-Fernandez | 2 | 531 | 37.65 |
| Josef Bigun | 3 | 426 | 41.34 |