Feature-level fusion in multimodal biometrics

Multimodal biometric systems utilize the evidence presented by multiple biometric modalities (e.g., face and fingerprint, multiple fingers of a user, multiple impressions of a single finger, etc.) in order to determine or verify the identity of an individual. Information from multiple sources can be consolidated in three distinct levels [1]: (i) feature set level; (ii) match score level; and (iii) decision level. While fusion at the match score and decision levels have been extensively studied in the literature, fusion at the feature level is a relatively understudied problem. A novel technique to perform fusion at the feature level by considering two biometric modalities---face and hand geometry, is presented in this paper. Also, a new distance metric conscripted as the Thresholded Absolute Distance (TAD) is used to help reinforce the system's robustness towards noise. Finally, two techniques are proposed to consolidate information available after match score fusion, with that obtained after feature set fusion. These techniques further enhance the performance of the multimodal biometric system and help find an approximate upper bound on its performance. Results indicate that the proposed techniques can lead to substantial improvement in multimodal matching abilities.

Name thesis:degree_name

MS

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Lane Department of Computer Science and Electrical Engineering

Year dc:date.available

2004

Author dc:creator

• Govindarajan, Rohin K.

Contributors dc:contributor

• Arun Abraham Ross.

• Computer science

Identifier

https://researchrepository.wvu.edu/etd/1489

OAI identifier oai:identifier

oai:researchrepository.wvu.edu:etd-2492