Back to Search
Start Over
Deep supervised class encoding for iris presentation attack detection
- Source :
- Digital Signal Processing. 121:103329
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- The vulnerabilities of a Biometric Authentication System (BAS) to spoof attacks have gained substantial attention over the years. Iris recognition, one of the most reliable and accurate BASs is vulnerable to various kinds of presentation attacks such as by printed iris or contact lenses. The presentation attack detection (PAD) becomes even more challenging when an attacker imposes variations by carrying out multiple spoofing attacks. To address this challenge, an end-to-end Deep Supervised Class Encoding (DSCE) approach for iris-PAD is proposed in this paper. This is to deal with three main attacks by (i) printed iris images, (ii) contact lenses, and (iii) synthetically generated iris images. DSCE is an autoencoder based supervised feature learning approach that exploits the class information, and minimizes the reconstruction and classification errors simultaneously during the training phase. DSCE is employed to design an iris-PAD framework termed as DeepI, to perceive counterfeit access to an iris-BAS. Experimental results on different benchmark databases show that DSCE based DeepI outperforms the current state-of-the-art iris-PAD methods. Also, in cross-database training-testing settings, the proposed approach manifests a promising generalization capability.
- Subjects :
- Spoofing attack
Biometrics
Computer science
business.industry
Applied Mathematics
Iris recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Pattern recognition
Class (biology)
Autoencoder
ComputingMethodologies_PATTERNRECOGNITION
Computational Theory and Mathematics
Artificial Intelligence
Encoding (memory)
Signal Processing
Benchmark (computing)
Computer Vision and Pattern Recognition
Artificial intelligence
Electrical and Electronic Engineering
Statistics, Probability and Uncertainty
business
Feature learning
Subjects
Details
- ISSN :
- 10512004
- Volume :
- 121
- Database :
- OpenAIRE
- Journal :
- Digital Signal Processing
- Accession number :
- edsair.doi...........9efcf5ac135a8c754cb47d7e8e23e186