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Face recognition has already been well studied under the visible light andthe infrared,in both intra-spectral and cross-spectral cases. However, how tofuse different light bands, i.e., hyperspectral face recognition, is still anopen research problem, which has the advantages of richer information retainingand all-weather functionality over single band face recognition. Among the veryfew works for hyperspectral face recognition, traditional non-deep learningtechniques are largely used. Thus, we in this paper bring deep learning intothe topic of hyperspectral face recognition, and propose a new fusion model(termed HyperFaceNet) especially for hyperspectral faces. The proposed fusionmodel is characterized by residual dense learning, a feedback style encoder anda recognition-oriented loss function. During the experiments, our method isproved to be of higher recognition rates than face recognition using eithervisible light or the infrared. Moreover, our fusion model is shown to besuperior to other general-purposed image fusion methods includingstate-of-the-arts, in terms of both image quality and recognition performance.

HyperFaceNet: A Hyperspectral Face Recognition Method Based on Deep Fusion