9,391 research outputs found
Influence of segmentation on deep iris recognition performance
Despite the rise of deep learning in numerous areas of computer vision and
image processing, iris recognition has not benefited considerably from these
trends so far. Most of the existing research on deep iris recognition is
focused on new models for generating discriminative and robust iris
representations and relies on methodologies akin to traditional iris
recognition pipelines. Hence, the proposed models do not approach iris
recognition in an end-to-end manner, but rather use standard heuristic iris
segmentation (and unwrapping) techniques to produce normalized inputs for the
deep learning models. However, because deep learning is able to model very
complex data distributions and nonlinear data changes, an obvious question
arises. How important is the use of traditional segmentation methods in a deep
learning setting? To answer this question, we present in this paper an
empirical analysis of the impact of iris segmentation on the performance of
deep learning models using a simple two stage pipeline consisting of a
segmentation and a recognition step. We evaluate how the accuracy of
segmentation influences recognition performance but also examine if
segmentation is needed at all. We use the CASIA Thousand and SBVPI datasets for
the experiments and report several interesting findings.Comment: 6 pages, 3 figures, 3 tables, submitted to IWBF 201
Constrained Design of Deep Iris Networks
Despite the promise of recent deep neural networks in the iris recognition
setting, there are vital properties of the classic IrisCode which are almost
unable to be achieved with current deep iris networks: the compactness of model
and the small number of computing operations (FLOPs). This paper re-models the
iris network design process as a constrained optimization problem which takes
model size and computation into account as learning criteria. On one hand, this
allows us to fully automate the network design process to search for the best
iris network confined to the computation and model compactness constraints. On
the other hand, it allows us to investigate the optimality of the classic
IrisCode and recent iris networks. It also allows us to learn an optimal iris
network and demonstrate state-of-the-art performance with less computation and
memory requirements
Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters
Data privacy is crucial when dealing with biometric data. Accounting for the
latest European data privacy regulation and payment service directive,
biometric template protection is essential for any commercial application.
Ensuring unlinkability across biometric service operators, irreversibility of
leaked encrypted templates, and renewability of e.g., voice models following
the i-vector paradigm, biometric voice-based systems are prepared for the
latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean
and cosine comparators are known to ensure data privacy demands, without loss
of discrimination nor calibration performance. Bridging gaps from template
protection to speaker recognition, two architectures are proposed for the
two-covariance comparator, serving as a generative model in this study. The
first architecture preserves privacy of biometric data capture subjects. In the
second architecture, model parameters of the comparator are encrypted as well,
such that biometric service providers can supply the same comparison modules
employing different key pairs to multiple biometric service operators. An
experimental proof-of-concept and complexity analysis is carried out on the
data from the 2013-2014 NIST i-vector machine learning challenge
Improved Heterogeneous Distance Functions
Instance-based learning techniques typically handle continuous and linear
input values well, but often do not handle nominal input attributes
appropriately. The Value Difference Metric (VDM) was designed to find
reasonable distance values between nominal attribute values, but it largely
ignores continuous attributes, requiring discretization to map continuous
values into nominal values. This paper proposes three new heterogeneous
distance functions, called the Heterogeneous Value Difference Metric (HVDM),
the Interpolated Value Difference Metric (IVDM), and the Windowed Value
Difference Metric (WVDM). These new distance functions are designed to handle
applications with nominal attributes, continuous attributes, or both. In
experiments on 48 applications the new distance metrics achieve higher
classification accuracy on average than three previous distance functions on
those datasets that have both nominal and continuous attributes.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
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