| Full text | |
| Author(s): |
Kuehlkamp, Andrey
[1]
;
Pinto, Allan
[2]
;
Rocha, Anderson
[2]
;
Bowyer, Kevin W.
[1]
;
Czajka, Adam
[1]
Total Authors: 5
|
| Affiliation: | [1] Univ Notre Dame, Dept Comp Sci & Engn, Notre Dame, IN 46556 - USA
[2] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP - Brazil
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | IEEE Transactions on Information Forensics and Security; v. 14, n. 6, p. 1419-1431, JUN 2019. |
| Web of Science Citations: | 4 |
| Abstract | |
The adoption of large-scale iris recognition systems around the world has brought to light the importance of detecting presentation attack images (textured contact lenses and printouts). This paper presents a new approach in iris presentation attack detection (PAD) by exploring combinations of convolutional neural networks (CNNs) and transformed input spaces through binarized statistical image features (BSIFs). Our method combines lightweight CNNs to classify multiple BSIF views of the input image. Following explorations on complementary input spaces leading to more discriminative features to detect presentation attacks, we also propose an algorithm to select the best (and most discriminative) predictors for the task at hand. An ensemble of predictors makes use of their expected individual performances to aggregate their results into a final prediction. Results show that this technique improves on the current state of the art in iris PAD, outperforming the winner of LivDet-Iris 2017 competition both for intra-and cross-dataset scenarios, and illustrating the very difficult nature of the cross-dataset scenario. (AU) | |
| FAPESP's process: | 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Projects - Thematic Grants |