HyperFaceNet: A Hyperspectral Face Recognition Method Based on Deep
Fusion
- URL: http://arxiv.org/abs/2008.00498v2
- Date: Sat, 12 Sep 2020 09:46:33 GMT
- Title: HyperFaceNet: A Hyperspectral Face Recognition Method Based on Deep
Fusion
- Authors: Zhicheng Cao, Xi Cen and Liaojun Pang
- Abstract summary: How to fuse different light bands, i.e., hyperspectral face recognition, is still an open research problem.
We propose a new fusion model (termed HyperFaceNet) especially for hyperspectral faces.
Our method is proved to be of higher recognition rates than face recognition using either visible light or the infrared.
- Score: 0.7734726150561088
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Face recognition has already been well studied under the visible light and
the infrared,in both intra-spectral and cross-spectral cases. However, how to
fuse different light bands, i.e., hyperspectral face recognition, is still an
open research problem, which has the advantages of richer information retaining
and all-weather functionality over single band face recognition. Among the very
few works for hyperspectral face recognition, traditional non-deep learning
techniques are largely used. Thus, we in this paper bring deep learning into
the topic of hyperspectral face recognition, and propose a new fusion model
(termed HyperFaceNet) especially for hyperspectral faces. The proposed fusion
model is characterized by residual dense learning, a feedback style encoder and
a recognition-oriented loss function. During the experiments, our method is
proved to be of higher recognition rates than face recognition using either
visible light or the infrared. Moreover, our fusion model is shown to be
superior to other general-purposed image fusion methods including
state-of-the-arts, in terms of both image quality and recognition performance.
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