Abstract
This paper presents a multimodal biometric system of fingerprint and ear biometrics. Scale Invariant Feature Transform (SIFT) descriptor based feature sets extracted from fingerprint and ear are fused. The fused set is encoded by K-medoids partitioning approach with less number of feature points in the set. K-medoids partition the whole dataset into clusters to minimize the error between data points belonging to the clusters and its center. Reduced feature set is used to match between two biometric sets. Matching scores are generated using wolf-lamb user-dependent feature weighting scheme introduced by Doddington. The technique is tested to exhibit its robust performance.
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© 2009 Springer-Verlag Berlin Heidelberg
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Kisku, D.R., Gupta, P., Sing, J.K. (2009). Feature Level Fusion of Biometrics Cues: Human Identification with Doddington’s Caricature. In: Ślęzak, D., Kim, Th., Fang, WC., Arnett, K.P. (eds) Security Technology. SecTech 2009. Communications in Computer and Information Science, vol 58. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10847-1_20
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DOI: https://doi.org/10.1007/978-3-642-10847-1_20
Publisher Name: Springer, Berlin, Heidelberg
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