Fusion of visual and infra-red face scores by weighted power series

Authors:
Kar-Ann Toh;Youngsung Kim;Sangyoun Lee;Jaihie Kim
Affiliations:
Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea;Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea;Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea;Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
Venue:
Pattern Recognition Letters
Year:
2008

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Abstract

This paper proposes a weighted power series model for face verification scores fusion. Essentially, a linear parametric power series model is adopted to directly minimize an approximated total error rate for fusion of multi-modal face verification scores. Unlike the conventional least-squares error minimization approach which involves fitting of a learning model to data density and then perform a threshold process for error counting, this work directly formulates the required target error count rate in terms of design model parameters with a closed-form solution. The solution is found to belong to a specific setting of the weighted least squares. Our experiments on fusing scores from visual and infra-red face images as well as on public data sets show promising results.