9 research outputs found
A Cryptanalysis of Two Cancelable Biometric Schemes based on Index-of-Max Hashing
Cancelable biometric schemes generate secure biometric templates by combining
user specific tokens and biometric data. The main objective is to create
irreversible, unlinkable, and revocable templates, with high accuracy in
matching. In this paper, we cryptanalyze two recent cancelable biometric
schemes based on a particular locality sensitive hashing function, index-of-max
(IoM): Gaussian Random Projection-IoM (GRP-IoM) and Uniformly Random
Permutation-IoM (URP-IoM). As originally proposed, these schemes were claimed
to be resistant against reversibility, authentication, and linkability attacks
under the stolen token scenario. We propose several attacks against GRP-IoM and
URP-IoM, and argue that both schemes are severely vulnerable against
authentication and linkability attacks. We also propose better, but not yet
practical, reversibility attacks against GRP-IoM. The correctness and practical
impact of our attacks are verified over the same dataset provided by the
authors of these two schemes.Comment: Some revisions and addition of acknowledgement
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port