Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art

03/31/2018
by   Adam Czajka, et al.
0

Iris recognition is increasingly used in large-scale applications. As a result, presentation attack detection for iris recognition takes on fundamental importance. This survey covers the diverse research literature on this topic. Different categories of presentation attack are described and placed in an application-relevant framework, and the state of the art in detecting each category of attack is summarized. One conclusion from this is that presentation attack detection for iris recognition is not yet a solved problem. Datasets available for research are described, research directions for the near- and medium-term future, and a short list of recommended readings are suggested.

READ FULL TEXT

page 6

page 7

research
06/23/2020

Iris Presentation Attack Detection: Where Are We Now?

As the popularity of iris recognition systems increases, the importance ...
research
10/29/2017

Synthetic Iris Presentation Attack using iDCGAN

Reliability and accuracy of iris biometric modality has prompted its lar...
research
10/23/2020

Attention-Guided Network for Iris Presentation Attack Detection

Convolutional Neural Networks (CNNs) are being increasingly used to addr...
research
06/12/2020

Multispectral Biometrics System Framework: Application to Presentation Attack Detection

In this work, we present a general framework for building a biometrics s...
research
11/25/2018

Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection

The adoption of large-scale iris recognition systems around the world ha...
research
10/28/2020

Micro Stripes Analyses for Iris Presentation Attack Detection

Iris recognition systems are vulnerable to the presentation attacks, suc...
research
08/22/2022

State Of The Art In Open-Set Iris Presentation Attack Detection

Research in presentation attack detection (PAD) for iris recognition has...

Please sign up or login with your details

Forgot password? Click here to reset