Quality-Based Conditional Processing in Multi-Biometrics: Application to
Sensor Interoperability
- URL: http://arxiv.org/abs/2211.13554v1
- Date: Thu, 24 Nov 2022 12:11:22 GMT
- Title: Quality-Based Conditional Processing in Multi-Biometrics: Application to
Sensor Interoperability
- Authors: Fernando Alonso-Fernandez, Julian Fierrez, Daniel Ramos, Joaquin
Gonzalez-Rodriguez
- Abstract summary: We describe and evaluate the ATVS-UAM fusion approach submitted to the quality-based evaluation of the 2007 BioSecure Multimodal Evaluation Campaign.
Our approach is based on linear logistic regression, in which fused scores tend to be log-likelihood-ratios.
Results show that the proposed approach outperforms all the rule-based fusion schemes.
- Score: 63.05238390013457
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As biometric technology is increasingly deployed, it will be common to
replace parts of operational systems with newer designs. The cost and
inconvenience of reacquiring enrolled users when a new vendor solution is
incorporated makes this approach difficult and many applications will require
to deal with information from different sources regularly. These
interoperability problems can dramatically affect the performance of biometric
systems and thus, they need to be overcome. Here, we describe and evaluate the
ATVS-UAM fusion approach submitted to the quality-based evaluation of the 2007
BioSecure Multimodal Evaluation Campaign, whose aim was to compare fusion
algorithms when biometric signals were generated using several biometric
devices in mismatched conditions. Quality measures from the raw biometric data
are available to allow system adjustment to changing quality conditions due to
device changes. This system adjustment is referred to as quality-based
conditional processing. The proposed fusion approach is based on linear
logistic regression, in which fused scores tend to be log-likelihood-ratios.
This allows the easy and efficient combination of matching scores from
different devices assuming low dependence among modalities. In our system,
quality information is used to switch between different system modules
depending on the data source (the sensor in our case) and to reject channels
with low quality data during the fusion. We compare our fusion approach to a
set of rule-based fusion schemes over normalized scores. Results show that the
proposed approach outperforms all the rule-based fusion schemes. We also show
that with the quality-based channel rejection scheme, an overall improvement of
25% in the equal error rate is obtained.
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