Abstract
In this paper, a novel three-layer Gabor-based network is proposed for heterogeneous face recognition. The input layer of our proposed network consists of pixel-wise image patches. At the hidden layer, a set of Gabor features are extracted by a projection operation and a magnitude function. Subsequently, a non-linear activation function is utilized after weighting the extracted Gabor features with random weight vectors. Finally, the output weights are deterministically learned similarly to that in extreme learning machine. Some experimental results on private BERC visual-thermal infrared database are observed and discussed. The proposed method shows promising results based on the average test recognition accuracy.
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