Abstract
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.
Original language | English |
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Pages (from-to) | 253-265 |
Number of pages | 13 |
Journal | Neurocomputing |
Volume | 261 |
DOIs | |
Publication status | Published - 2017 Oct 25 |
Bibliographical note
Funding Information:This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant numbers: NRF-2012R1A1A2042428 and NRF-2015R1D1A1A09061316 ).
Publisher Copyright:
© 2017 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence