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A Gabor-based network for heterogeneous face recognition
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DOI：10.1016/j.neucom.2015.11.137.png)
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摘要
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In this paper, we propose a single hidden-layer Gabor-based network for heterogeneous face recognition. The proposed input layer contains novel computational units which propagate geometrically localized input image sub-blocks to hidden nodes. The propagated pixels are then convolved with a set of Gabor kernels followed by a randomly weighted summation and a non-linear activation function operation. The output layer adopts a linear weighting scheme which can be deterministically estimated similar to that in extreme learning machine. Our experiments on three experimental scenarios using BERC visual-thermal infrared database and CASIA visual-near infrared database show promising results for the proposed network. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Heterogeneous face recognition
Gabor features
Extreme learning machine
Random weighting
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IF:
6.5
论文数:
2.5W
被引数:
6.5W
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