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Cascaded multimodal biometric recognition framework
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Journal Article
Cascaded multimodal biometric recognition framework
2014
Overview
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.
Publisher
The Institution of Engineering and Technology,Institution of Engineering and Technology,John Wiley & Sons, Inc
Subject
/ Biometric recognition systems
/ cascaded multimodal biometric recognition framework
/ Cascades
/ Exact sciences and technology
/ Information, signal and communications theory
/ Lists
/ Mahalanobis distance‐based match score quality measure
/ multimodal cascaded classifier‐based approaches
/ Signal and communications theory
/ Signal representation. Spectral analysis
