550 research outputs found
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
Forensic Iris Image Synthesis
Post-mortem iris recognition is an emerging application of iris-based human
identification in a forensic setup, able to correctly identify deceased
subjects even three weeks post-mortem. This technique thus is considered as an
important component of future forensic toolkits. The current advancements in
this field are seriously slowed down by exceptionally difficult data
collection, which can happen in mortuary conditions, at crime scenes, or in
``body farm'' facilities. This paper makes a novel contribution to facilitate
progress in post-mortem iris recognition by offering a conditional
StyleGAN-based iris synthesis model, trained on the largest-available dataset
of post-mortem iris samples acquired from more than 350 subjects, generating --
through appropriate exploration of StyleGAN latent space -- multiple
within-class (same identity) and between-class (different new identities)
post-mortem iris images, compliant with ISO/IEC 29794-6, and with decomposition
deformations controlled by the requested PMI (post mortem interval). Besides an
obvious application to enhance the existing, very sparse, post-mortem iris
datasets to advance -- among others -- iris presentation attack endeavors, we
anticipate it may be useful to generate samples that would expose professional
forensic human examiners to never-seen-before deformations for various PMIs,
increasing their training effectiveness. The source codes and model weights are
made available with the paper
The impact of collarette region-based convolutional neural network for iris recognition
Iris recognition is a biometric technique that reliably and quickly recognizes a person by their iris based on unique biological characteristics. Iris has an exceptional structure and it provides very rich feature spaces as freckles, stripes, coronas, zigzag collarette area, etc. It has many features where its growing interest in biometric recognition lies. This paper proposes an improved iris recognition method for person identification based on Convolutional Neural Networks (CNN) with an improved recognition rate based on a contribution on zigzag collarette area - the area surrounding the pupil - recognition. Our work is in the field of biometrics especially iris recognition; the iris recognition rate using the full circle of the zigzag collarette was compared with the detection rate using the lower semicircle of the zigzag collarette. The classification of the collarette is based on the Alex-Net model to learn this feature, the use of the couple (collarette/CNN) allows for noiseless and more targeted characterization and also an automatic extraction of the lower semicircle of the collarette region, finally, the SVM training model is used for classification using grayscale eye image data taken from (CASIA-iris-V4) database. The experimental results show that our contribution proves to be the best accurate, because the CNN can effectively extract the image features with higher classification accuracy and because our new method, which uses the lower semicircle of the collarette region, achieved the highest recognition accuracy compared with the old methods that use the full circle of collarette region
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