51,123 research outputs found
Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking
Morphing attacks have posed a severe threat to Face Recognition System (FRS).
Despite the number of advancements reported in recent works, we note serious
open issues such as independent benchmarking, generalizability challenges and
considerations to age, gender, ethnicity that are inadequately addressed.
Morphing Attack Detection (MAD) algorithms often are prone to generalization
challenges as they are database dependent. The existing databases, mostly of
semi-public nature, lack in diversity in terms of ethnicity, various morphing
process and post-processing pipelines. Further, they do not reflect a realistic
operational scenario for Automated Border Control (ABC) and do not provide a
basis to test MAD on unseen data, in order to benchmark the robustness of
algorithms. In this work, we present a new sequestered dataset for facilitating
the advancements of MAD where the algorithms can be tested on unseen data in an
effort to better generalize. The newly constructed dataset consists of facial
images from 150 subjects from various ethnicities, age-groups and both genders.
In order to challenge the existing MAD algorithms, the morphed images are with
careful subject pre-selection created from the contributing images, and further
post-processed to remove morphing artifacts. The images are also printed and
scanned to remove all digital cues and to simulate a realistic challenge for
MAD algorithms. Further, we present a new online evaluation platform to test
algorithms on sequestered data. With the platform we can benchmark the morph
detection performance and study the generalization ability. This work also
presents a detailed analysis on various subsets of sequestered data and
outlines open challenges for future directions in MAD research.Comment: This paper is a pre-print. The article is accepted for publication in
IEEE Transactions on Information Forensics and Security (TIFS
Data-Dependent Stability of Stochastic Gradient Descent
We establish a data-dependent notion of algorithmic stability for Stochastic
Gradient Descent (SGD), and employ it to develop novel generalization bounds.
This is in contrast to previous distribution-free algorithmic stability results
for SGD which depend on the worst-case constants. By virtue of the
data-dependent argument, our bounds provide new insights into learning with SGD
on convex and non-convex problems. In the convex case, we show that the bound
on the generalization error depends on the risk at the initialization point. In
the non-convex case, we prove that the expected curvature of the objective
function around the initialization point has crucial influence on the
generalization error. In both cases, our results suggest a simple data-driven
strategy to stabilize SGD by pre-screening its initialization. As a corollary,
our results allow us to show optimistic generalization bounds that exhibit fast
convergence rates for SGD subject to a vanishing empirical risk and low noise
of stochastic gradient
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