English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Single sensor-based multi-modal biometrics is a promising approach that offers simple system construction, low cost, and wide applicability to real situations such as CCTV footage-based criminal investigations. In multi-modal biometrics, fusion at the score-level is a popular and promising approach, and data qualities that affect the matching score of each modality are often incorporated as a quality-dependent score-level fusion framework. This paper presents a very large-scale single sensor-based multi-quality multi-modal biometric score database called MultiQ Score Database version 2 to advance the research into evaluation, comparison, and benchmarking of score-level fusion approaches using both quality-independent and quality-dependent protocols. We extracted gait, head, and height modalities from the OU-ISIR Gait Database and introduce spatial resolution (SR), temporal resolution (TR) and view as quality measures that significantly affect biometric system performance. We considered seven and 10 scaling factors for SR and TR, respectively, with four view variations. We then constructed a database comprising approximately 4 million genuine and 7.5 billion imposter score databases. To evaluate this database, we set two different protocols, and provided a set recognition accuracy for state-of-the-art approaches using protocols for both quality-independent and quality-dependent schemes. This database and the evaluation results will be beneficial for score-level fusion research. Additionally, we provide detailed analysis of the recognition accuracies associated with gait, head, and height modalities in different spatial/temporal resolutions and views. These analyses may be useful in criminal investigation research.

MultiQ: single sensor-based multi-quality multi-modal large-scale biometric score database and its performance evaluation