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In this paper, a novel class-dependence feature analysis method based on Correlation Filter Bank (CFB) technique for effective multimodal biometrics fusion at the feature level is developed. In CFB, the unconstrained correlation filter trained for a specific modality is designed by optimizing the overall original correlation outputs. Therefore, the differences between modalities have been taken into account and useful information in various modalities is fully exploited. Preliminary experimental results on the fusion of face and palmprint biometrics show the superiority of the novel method.
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