NDLI: A framework for quality-based biometric classifier selection

Content Provider	IEEE Xplore Digital Library
Author	Bhatt, H.S. Bharadwaj, S. Vatsa, M. Singh, R. Ross, A. Noore, A.
Copyright Year	2011
Description	Author affiliation: West Virginia University, USA (Ross, A.; Noore, A.) \|\| IIIT Delhi, India (Bhatt, H.S.; Bharadwaj, S.; Vatsa, M.; Singh, R.)
Abstract	Multibiometric systems fuse the evidence (e.g., match scores) pertaining to multiple biometric modalities or classifiers. Most score-level fusion schemes discussed in the literature require the processing (i.e., feature extraction and matching) of every modality prior to invoking the fusion scheme. This paper presents a framework for dynamic classifier selection and fusion based on the quality of the gallery and probe images associated with each modality with multiple classifiers. The quality assessment algorithm for each biometric modality computes a quality vector for the gallery and probe images that is used for classifier selection. These vectors are used to train Support Vector Machines (SVMs) for decision making. In the proposed framework, the biometric modalities are arranged sequentially such that the stronger biometric modality has higher priority for being processed. Since fusion is required only when all unimodal classifiers are rejected by the SVM classifiers, the average computational time of the proposed framework is significantly reduced. Experimental results on different multi-modal databases involving face and fingerprint show that the proposed quality-based classifier selection framework yields good performance even when the quality of the biometric sample is sub-optimal.
Starting Page	1
Ending Page	7
File Size	1042927
Page Count	7
File Format	PDF
ISBN	9781457713583
e-ISBN	9781457713590
e-ISBN	9781457713576
DOI	10.1109/IJCB.2011.6117518
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image edge detection Noise Face Probes
Content Type	Text
Resource Type	Article

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