Extended feature-fusion guidelines to improve image-based multi-modal biometrics

Abstract

The feature-level, unlike the match score-level, lacks multi-modal fusion guidelines. This work demonstrates a practical approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature-level for improved human identification accuracy. Feature-fusion guidelines, proposed in recent work, are extended by adding the palmprint modality and the support vector machine classifier. Guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature-level to reduce the equal error rate on the SDUMLA and IITD datasets, using a novel feature-fusion methodology.