4,111 research outputs found
On Generative Adversarial Network Based Synthetic Iris Presentation Attack And Its Detection
Human iris is considered a reliable and accurate modality for biometric recognition due to its unique texture information. Reliability and accuracy of iris biometric modality have prompted its large-scale deployment for critical applications such as border control and national identification projects. The extensive growth of iris recognition systems has raised apprehensions about the susceptibility of these systems to various presentation attacks.
In this thesis, a novel iris presentation attack using deep learning based synthetically generated iris images is presented. Utilizing the generative capability of deep convolutional generative adversarial networks and iris quality metrics, a new framework, named as iDCGAN is proposed for creating realistic appearing synthetic iris images. In-depth analysis is performed using quality score distributions of real and synthetically generated iris images to understand the effectiveness of the proposed approach. We also demonstrate that synthetically generated iris images can be used to attack existing iris recognition systems.
As synthetically generated iris images can be effectively deployed in iris presentation attacks, it is important to develop accurate iris presentation attack detection algorithms which can distinguish such synthetic iris images from real iris images. For this purpose, a novel structural and textural feature-based iris presentation attack detection framework (DESIST) is proposed. The key emphasis of DESIST is on developing a unified framework for detecting a medley of iris presentation attacks, including synthetic iris. Experimental evaluations showcase the efficacy of the proposed DESIST framework in detecting synthetic iris presentation attacks
A Benchmark for Iris Location and a Deep Learning Detector Evaluation
The iris is considered as the biometric trait with the highest unique
probability. The iris location is an important task for biometrics systems,
affecting directly the results obtained in specific applications such as iris
recognition, spoofing and contact lenses detection, among others. This work
defines the iris location problem as the delimitation of the smallest squared
window that encompasses the iris region. In order to build a benchmark for iris
location we annotate (iris squared bounding boxes) four databases from
different biometric applications and make them publicly available to the
community. Besides these 4 annotated databases, we include 2 others from the
literature. We perform experiments on these six databases, five obtained with
near infra-red sensors and one with visible light sensor. We compare the
classical and outstanding Daugman iris location approach with two window based
detectors: 1) a sliding window detector based on features from Histogram of
Oriented Gradients (HOG) and a linear Support Vector Machines (SVM) classifier;
2) a deep learning based detector fine-tuned from YOLO object detector.
Experimental results showed that the deep learning based detector outperforms
the other ones in terms of accuracy and runtime (GPUs version) and should be
chosen whenever possible.Comment: Accepted for presentation at the International Joint Conference on
Neural Networks (IJCNN) 201
- …