Single sensor-based multi-quality multi-modal biometric score database and its performance evaluation

Abstract

We constructed a large-scale multi-quality multi-modal biometric score database to advance studies on quality-dependent score-level fusion. In particular, we focused on single sensor-based multi-modal biometrics because of their advantages of simple system construction, low cost, and wide availability in real situations such as CCTV footage-based criminal investigation, unlike conventional individual sensor-based multi-modal biometrics that require multiple sensors. As for the modalities of multiple biometrics, we extracted gait, head, and the height biometrics from a single walking image sequence, and considered spatial resolution (SR) and temporal resolution (TR) as quality measures that simultaneously affect the scores of individual modalities. We then computed biometric scores of 1912 subjects under a total of 130 combinations of the quality measures, i.e., 13 SRs and 10 TRs, and constructed a very large-scale biometric score database composed of 1,814,488 genuine scores and 3,467,486,568 imposter scores. We finally provide performance evaluation results both for quality-independent and quality-dependent score-level fusion approaches using two protocols that will be beneficial to the score-level fusion research community.