140 research outputs found
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
BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images
Current state-of-the-art segmentation techniques for ocular images are
critically dependent on large-scale annotated datasets, which are
labor-intensive to gather and often raise privacy concerns. In this paper, we
present a novel framework, called BiOcularGAN, capable of generating synthetic
large-scale datasets of photorealistic (visible light and near-infrared) ocular
images, together with corresponding segmentation labels to address these
issues. At its core, the framework relies on a novel Dual-Branch StyleGAN2
(DB-StyleGAN2) model that facilitates bimodal image generation, and a Semantic
Mask Generator (SMG) component that produces semantic annotations by exploiting
latent features of the DB-StyleGAN2 model. We evaluate BiOcularGAN through
extensive experiments across five diverse ocular datasets and analyze the
effects of bimodal data generation on image quality and the produced
annotations. Our experimental results show that BiOcularGAN is able to produce
high-quality matching bimodal images and annotations (with minimal manual
intervention) that can be used to train highly competitive (deep) segmentation
models (in a privacy aware-manner) that perform well across multiple real-world
datasets. The source code for the BiOcularGAN framework is publicly available
at https://github.com/dariant/BiOcularGAN.Comment: 13 pages, 14 figure
RT-BENE: A Dataset and Baselines for Real-Time Blink Estimation in Natural Environments
In recent years gaze estimation methods have made substantial progress, driven by the numerous application areas including human-robot interaction, visual attention estimation and foveated rendering for virtual reality headsets. However, many gaze estimation methods typically assume that the subject's eyes are open; for closed eyes, these methods provide irregular gaze estimates. Here, we address this assumption by first introducing a new open-sourced dataset with annotations of the eye-openness of more than 200,000 eye images, including more than 10,000 images where the eyes are closed. We further present baseline methods that allow for blink detection using convolutional neural networks. In extensive experiments, we show that the proposed baselines perform favourably in terms of precision and recall. We further incorporate our proposed RT-BENE baselines in the recently presented RT-GENE gaze estimation framework where it provides a real-time inference of the openness of the eyes. We argue that our work will benefit both gaze estimation and blink estimation methods, and we take steps towards unifying these methods
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