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Recently developed matrix-based complex PCA (MCPCA) can fuse bimodal biometrics at the feature level and obtain a promising classification accuracy. However, when solving the eigen-equation, MCPCA is only a unsupervised method, which implies that in the training process MCPCA is not able to exploit the class labels of training samples. In order to overcome this problem, we propose an improvement to...
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