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Using Score Fusion for Improving the Performance of Multispectral Face Recognition

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Signal and Image Processing for Biometrics

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 292))

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Abstract

Score fusion combines several scores from multiple modalities and/or multiple matchers, which can increase the accuracy of face recognition meanwhile decrease false accept rate (FAR). Specifically, the face scores are generated from two-spectral bands (visible and thermal) and from three matchers (circular Gaussian filter, face pattern byte, elastic bunch graphic matching). In this chapter, we first review the three face recognition algorithms (matchers), then present and compare the fusion performance of seven fusion methods: linear discriminant analysis (LDA), k-nearest neighbor (KNN), artificial neural network (ANN), support vector machine (SVM), binomial logistic regression (BLR), Gaussian mixture model (GMM), and hidden Markov model (HMM). Our experiments are conducted with the Alcon State University Multispectral face dataset that currently consists of two spectral images from 105 subjects. The experimental results show that all score fusions can improve the accuracy meanwhile reduce the FAR, and the KNN score fusion gives the best performance.

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Acknowledgments

The current research is funded by the Department of Defense Research and Education. The thermal face recognition research was previously supported by the Department of Homeland Security.

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Authors and Affiliations

  1. Alcorn State University, Lorman, MS, USA

    Yufeng Zheng

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  1. Yufeng Zheng
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Correspondence to Yufeng Zheng .

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Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Jacob Scharcanski

  2. IT-Instituto de Telecomunicações, University of Beira Interior, Covilhã, Portugal

    Hugo Proença

  3. Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, USA

    Eliza Du

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Zheng, Y. (2014). Using Score Fusion for Improving the Performance of Multispectral Face Recognition. In: Scharcanski, J., Proença, H., Du, E. (eds) Signal and Image Processing for Biometrics. Lecture Notes in Electrical Engineering, vol 292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54080-6_5

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  • DOI: https://doi.org/10.1007/978-3-642-54080-6_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54079-0

  • Online ISBN: 978-3-642-54080-6

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