Reliability and accuracy of iris biometric modality has prompted its large-scale deployment for critical applications such as border control and national ID projects. The extensive growth of iris recognition systems has raised apprehensions about susceptibility of these systems to various attacks. In the past, researchers have examined the impact of various iris presentation attacks such as textured contact lenses and print attacks. In this research, we present a novel presentation attack using deep learning based synthetic iris generation. Utilizing the generative capability of deep convolutional generative adversarial networks and iris quality metrics, we propose a new framework, named as iDCGAN (iris deep convolutional generative adversarial network) for generating realistic appearing synthetic iris images. We demonstrate the effect of these synthetically generated iris images as presentation attack on iris recognition by using a commercial system. The state-of-the-art presentation attack detection framework, DESIST is utilized to analyze if it can discriminate these synthetically generated iris images from real images. The experimental results illustrate that mitigating the proposed synthetic presentation attack is of paramount importance.
Gender and ethnicity classification of Iris images using deep class-encoder
Soft biometric modalities have shown their utility in different
applications including reducing the search space significantly.
This leads to improved recognition performance,
reduced computation time, and faster processing of test
samples. Some common soft biometric modalities are ethnicity,
gender, age, hair color, iris color, presence of facial
hair or moles, and markers. This research focuses on performing
ethnicity and gender classification on iris images.
We present a novel supervised autoencoder based approach,
Deep Class-Encoder, which uses class labels to learn discriminative
representation for the given sample by mapping
the learned feature vector to its label. The proposed model
is evaluated on two datasets each for ethnicity and gender
classification. The results obtained using the proposed
Deep Class-Encoder demonstrate its effectiveness in comparison
to existing approaches and state-of-the-art methods.
- Publication Date:
- NSF-PAR ID:
- 10053778
- Journal Name:
- International Joint Conference on Biometrics (IJCB)
- Page Range or eLocation-ID:
- 666 to 673
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1109/BTAS.2017.8272755
