Feature-Fusion Guidelines for Image-Based Multi-Modal Biometric Fusion

Abstract

<jats:p>The feature level, unlike the match score level, lacks multi-modal fusion guidelines. This work demonstrates a new 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 our recent work, are extended by adding a new face segmentation method and the support vector machine classifier. The new face segmentation method improves the face identification equal error rate (EER) by 10%. The support vector machine classifier combined with the new feature selection approach, proposed in our recent work, outperforms other classifiers when using a single training sample. Feature-fusion 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, using a novel feature-fusion methodology, reducing the EER of two groups of three datasets namely: SDUMLA face, SDUMLA fingerprint and IITD palmprint; MUCT Face, MCYT Fingerprint and CASIA Palmprint.</jats:p>

Analyzing the Effects of Data Augmentation on Single and Multimodal Biometrics

“…The score-level fusion is basically used to make the biometric systems more accurate and reliable, It is efficient when one of the input biometric traits are not present in fully correct format like scars on fingerprints or change of the voice due to weather conditions and the system is still required to recognize the users reliably. Likewise, the features from two or more biometric traits may also be fused together to generate a new combined feature set based on those modalities [5]. The feature level fusion scheme combines features from one or more biometric modalities and create a single new biometric template that consists of the properties from both of the fused modalities or biometric traits.…”