705 research outputs found
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets
A data augmentation methodology is presented and applied to generate a large
dataset of off-axis iris regions and train a low-complexity deep neural
network. Although of low complexity the resulting network achieves a high level
of accuracy in iris region segmentation for challenging off-axis eye-patches.
Interestingly, this network is also shown to achieve high levels of performance
for regular, frontal, segmentation of iris regions, comparing favorably with
state-of-the-art techniques of significantly higher complexity. Due to its
lower complexity, this network is well suited for deployment in embedded
applications such as augmented and mixed reality headsets
Data-Driven Segmentation of Post-mortem Iris Images
This paper presents a method for segmenting iris images obtained from the
deceased subjects, by training a deep convolutional neural network (DCNN)
designed for the purpose of semantic segmentation. Post-mortem iris recognition
has recently emerged as an alternative, or additional, method useful in
forensic analysis. At the same time it poses many new challenges from the
technological standpoint, one of them being the image segmentation stage, which
has proven difficult to be reliably executed by conventional iris recognition
methods. Our approach is based on the SegNet architecture, fine-tuned with
1,300 manually segmented post-mortem iris images taken from the
Warsaw-BioBase-Post-Mortem-Iris v1.0 database. The experiments presented in
this paper show that this data-driven solution is able to learn specific
deformations present in post-mortem samples, which are missing from alive
irises, and offers a considerable improvement over the state-of-the-art,
conventional segmentation algorithm (OSIRIS): the Intersection over Union (IoU)
metric was improved from 73.6% (for OSIRIS) to 83% (for DCNN-based presented in
this paper) averaged over subject-disjoint, multiple splits of the data into
train and test subsets. This paper offers the first known to us method of
automatic processing of post-mortem iris images. We offer source codes with the
trained DCNN that perform end-to-end segmentation of post-mortem iris images,
as described in this paper. Also, we offer binary masks corresponding to manual
segmentation of samples from Warsaw-BioBase-Post-Mortem-Iris v1.0 database to
facilitate development of alternative methods for post-mortem iris
segmentation
Mask-guided Style Transfer Network for Purifying Real Images
Recently, the progress of learning-by-synthesis has proposed a training model
for synthetic images, which can effectively reduce the cost of human and
material resources. However, due to the different distribution of synthetic
images compared with real images, the desired performance cannot be achieved.
To solve this problem, the previous method learned a model to improve the
realism of the synthetic images. Different from the previous methods, this
paper try to purify real image by extracting discriminative and robust features
to convert outdoor real images to indoor synthetic images. In this paper, we
first introduce the segmentation masks to construct RGB-mask pairs as inputs,
then we design a mask-guided style transfer network to learn style features
separately from the attention and bkgd(background) regions and learn content
features from full and attention region. Moreover, we propose a novel
region-level task-guided loss to restrain the features learnt from style and
content. Experiments were performed using mixed studies (qualitative and
quantitative) methods to demonstrate the possibility of purifying real images
in complex directions. We evaluate the proposed method on various public
datasets, including LPW, COCO and MPIIGaze. Experimental results show that the
proposed method is effective and achieves the state-of-the-art results.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0582
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