Quality-Based Conditional Processing in Multi-Biometrics: Application to Sensor Interoperability

TL;DR

This paper presents a quality-based fusion method for multi-biometrics that improves sensor interoperability by adjusting processing based on data quality, leading to significant performance gains in biometric systems.

Contribution

It introduces a novel quality-based conditional processing approach using linear logistic regression for biometric score fusion, enhancing interoperability across different sensors.

Findings

Outperforms rule-based fusion schemes in biometric score combination.

Achieves 25% improvement in equal error rate with quality-based channel rejection.

Demonstrates effective handling of mismatched biometric sensor data.

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.