HyperFaceNet: A Hyperspectral Face Recognition Method Based on Deep Fusion

TL;DR

HyperFaceNet introduces a deep learning fusion model for hyperspectral face recognition, leveraging residual dense learning and feedback encoding to outperform traditional methods and single-band recognition in accuracy and image quality.

Contribution

This paper presents HyperFaceNet, a novel deep fusion model specifically designed for hyperspectral face recognition, incorporating residual dense learning and feedback encoding.

Findings

Higher recognition rates than visible or infrared face recognition.

Superior image quality and recognition performance compared to other fusion methods.

Effective deep learning approach for hyperspectral face recognition.

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.