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In biometrics fusion, the match score level fusion has been frequently adopted because it contains the richest information regarding the input pattern. However, in practice, the size of training match scores increases almost exponentially with respect to the number of users. Under this situation, the cost of learning computation and memory usage can be very high. In this paper, the authors propose an online learning algorithm to resolve the computational problem. While the existing recursive least squares learning approach contains a mismatch between its objective function and the desired classification performance, the proposed online learning directly optimizes the classification performance with respect to fusion classifier design.
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