Iris presentation attack detection based on best-k feature selection from YOLO inspired RoI
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Title
Iris presentation attack detection based on best-k feature selection from YOLO inspired RoI
Authors
Keywords
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Journal
NEURAL COMPUTING & APPLICATIONS
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-09-22
DOI
10.1007/s00521-020-05342-3
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Note: Only part of the references are listed.- Biometric spoofing: Iris presentation attack detection and contact lens discrimination through score-level fusion
- (2020) Meenakshi Choudhary et al. APPLIED SOFT COMPUTING
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- (2018) Sheng-Hsun Hsieh et al. SENSORS
- Cosmetic Detection Framework for Face and Iris Biometrics
- (2018) Omid Sharifi et al. Symmetry-Basel
- Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection
- (2018) Andrey Kuehlkamp et al. IEEE Transactions on Information Forensics and Security
- Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network
- (2017) Fei He et al. JOURNAL OF ELECTRONIC IMAGING
- Iris liveness detection using regional features
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- (2016) Diego Gragnaniello et al. PATTERN RECOGNITION LETTERS
- Pupil Dynamics for Iris Liveness Detection
- (2015) Adam Czajka IEEE Transactions on Information Forensics and Security
- An Investigation of Local Descriptors for Biometric Spoofing Detection
- (2015) Diego Gragnaniello et al. IEEE Transactions on Information Forensics and Security
- Robust Scheme for Iris Presentation Attack Detection Using Multiscale Binarized Statistical Image Features
- (2015) R. Raghavendra et al. IEEE Transactions on Information Forensics and Security
- Deep Representations for Iris, Face, and Fingerprint Spoofing Detection
- (2015) David Menotti et al. IEEE Transactions on Information Forensics and Security
- Eye movement-driven defense against iris print-attacks
- (2015) Ioannis Rigas et al. PATTERN RECOGNITION LETTERS
- Robust Detection of Textured Contact Lenses in Iris Recognition Using BSIF
- (2015) James S. Doyle et al. IEEE Access
- Accurate Iris Recognition at a Distance Using Stabilized Iris Encoding and Zernike Moments Phase Features
- (2014) Chun-Wei Tan et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Unraveling the Effect of Textured Contact Lenses on Iris Recognition
- (2014) Daksha Yadav et al. IEEE Transactions on Information Forensics and Security
- DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo
- (2009) E. Tola et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- A comprehensive review of current local features for computer vision
- (2008) Jing Li et al. NEUROCOMPUTING
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