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.
Bibliographical noteFunding Information:
This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence