2 research outputs found
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
Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks
The iris can be considered as one of the most important biometric traits due
to its high degree of uniqueness. Iris-based biometrics applications depend
mainly on the iris segmentation whose suitability is not robust for different
environments such as near-infrared (NIR) and visible (VIS) ones. In this paper,
two approaches for robust iris segmentation based on Fully Convolutional
Networks (FCNs) and Generative Adversarial Networks (GANs) are described.
Similar to a common convolutional network, but without the fully connected
layers (i.e., the classification layers), an FCN employs at its end a
combination of pooling layers from different convolutional layers. Based on the
game theory, a GAN is designed as two networks competing with each other to
generate the best segmentation. The proposed segmentation networks achieved
promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4,
IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in
both non-cooperative and cooperative domains, outperforming the baselines
techniques which are the best ones found so far in the literature, i.e., a new
state of the art for these datasets. Furthermore, we manually labeled 2,431
images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks
available for research purposes.Comment: Accepted for presentation at the Conference on Graphics, Patterns and
Images (SIBGRAPI) 201