Open AccessCascaded multimodal biometric recognition framework
Author(s) -
Baig Asim,
Bouridane Ahmed,
Kurugollu Fatih,
Albesher Badr
Publication year - 2014
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
ISSN - 2047-4946
DOI - 10.1049/iet-bmt.2012.0043
Subject(s) - computer science , biometrics , classifier (uml) , artificial intelligence , mahalanobis distance , pattern recognition (psychology) , cascading classifiers , machine learning , random subspace method
A practically viable multi‐biometric recognition system should not only be stable, robust and accurate but should also adhere to real‐time processing speed and memory constraints. This study proposes a cascaded classifier‐based framework for use in biometric recognition systems. The proposed framework utilises a set of weak classifiers to reduce the enrolled users’ dataset to a small list of candidate users. This list is then used by a strong classifier set as the final stage of the cascade to formulate the decision. At each stage, the candidate list is generated by a Mahalanobis distance‐based match score quality measure. One of the key features of the authors framework is that each classifier in the ensemble can be designed to use a different modality thus providing the advantages of a truly multimodal biometric recognition system. In addition, it is one of the first truly multimodal cascaded classifier‐based approaches for biometric recognition. The performance of the proposed system is evaluated both for single and multimodalities to demonstrate the effectiveness of the approach.