653 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
MENTOR: Human Perception-Guided Pretraining for Iris Presentation Detection
Incorporating human salience into the training of CNNs has boosted
performance in difficult tasks such as biometric presentation attack detection.
However, collecting human annotations is a laborious task, not to mention the
questions of how and where (in the model architecture) to efficiently
incorporate this information into model's training once annotations are
obtained. In this paper, we introduce MENTOR (huMan pErceptioN-guided
preTraining fOr iris pResentation attack detection), which addresses both of
these issues through two unique rounds of training. First, we train an
autoencoder to learn human saliency maps given an input iris image (both real
and fake examples). Once this representation is learned, we utilize the trained
autoencoder in two different ways: (a) as a pre-trained backbone for an iris
presentation attack detector, and (b) as a human-inspired annotator of salient
features on unknown data. We show that MENTOR's benefits are threefold: (a)
significant boost in iris PAD performance when using the human
perception-trained encoder's weights compared to general-purpose weights (e.g.
ImageNet-sourced, or random), (b) capability of generating infinite number of
human-like saliency maps for unseen iris PAD samples to be used in any human
saliency-guided training paradigm, and (c) increase in efficiency of iris PAD
model training. Sources codes and weights are offered along with the paper.Comment: 8 pages, 3 figure
Indexing Irises by Intrinsic Dimension
28,000+ high-quality iris images of 1350 distinct eyes from 650+ different
individuals from a relatively diverse university town population were
collected. A small defined unobstructed portion of the normalized iris image is
selected as a key portion for quickly identifying an unknown individual when
submitting an iris image to be matched to a database of enrolled irises of the
1350 distinct eyes. The intrinsic dimension of a set of these key portions of
the 1350 enrolled irises is measured to be about four (4). This set is mapped
to a four-dimensional intrinsic space by principal components analysis (PCA).
When an iris image is presented to the iris database for identification, the
search begins in the neighborhood of the location of its key portion in the 4D
intrinsic space, typically finding a correct identifying match after comparison
to only a few percent of the database.Comment: 5 pages, 6 figure
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