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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

—Personal verification system that uses a single biometric trait often faces numerous limitations such as noisy sensor data, non-universality, non-distinctiveness and spoof attack. These limitations can be overcome by multimodal biometric systems that consolidate the evidence presented by multiple biometric sources and typically has better recognition performance compared to systems based on a single biometric modality. This study proposes fusion of face and fingerprint for robust recognition system. The integration is performed at the matching score level. The matching tasks for both modalities are carried out by using support vector machines (SVM) as the classifier. Experiments on face expression and fingerprint database show that the performances of multimodal biometric system provide better recognition compared to single biometric modality. Based on the fusion techniques evaluated, trait-specific weight was found to be highly effective than the sum rule-based fusion. Equal error rate (EER) percentage for face- only and fingerprint- only systems are 2.50% and 5.56%, respectively, while the EER for system using sum rule- based fusion and system using trait-specific weights are 0.833% and 0.340%, respectively.

Fusion of Face and Fingerprint for Robust Personal Verification System