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• W2313504670 abstract "Face images captured in different spectral bands are said to be heterogeneous. In this paper, we propose a new approach based on subspace-mapping for heterogeneous face recognition and synthesis. In the recognition section, Local Binary Pattern (LBP) is used as facial representation for near infrared (NIR), visual light (VIS) and 3D range images. Then Canonical Correlation Analysis (CCA) is applied to learn the mapping between the different LBP-face patterns. The corresponding matching scores are calculated in the CCA subspace for the final decision. In the synthesis section, according to the CCA transformation matrices obtained above, we apply ridge regression to determine an approximate linear relationship between the target pattern image and the projection vector of the probe. Experimental results are provided to evaluate the accuracy of the method. The work shows a practical solution for reliable heterogeneous face synthesis." @default.
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• W2313504670 date "2012-05-31" @default.
• W2313504670 modified "2023-09-27" @default.
• W2313504670 title "Heterogeneous Face Recognition and Synthesis Using Canonical Correlation Analysis" @default.
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• W2313504670 doi "https://doi.org/10.4156/jcit.vol7.issue8.45" @default.
• W2313504670 hasPublicationYear "2012" @default.
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