Li Wenlong, Cen Xi, Pang Liaojun, Cao Zhicheng
Molecular and Neuroimaging Engineering Research Center of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an 710126, China.
School of Telecommunications Engineering, Xidian University, Xi'an 710126, China.
Sensors (Basel). 2024 Apr 27;24(9):2785. doi: 10.3390/s24092785.
Face recognition has been well studied under visible light and infrared (IR) in both intra-spectral and cross-spectral cases. However, how to fuse different light bands for face recognition, i.e., hyperspectral face recognition, is still an open research problem, which has the advantages of richer information retention and all-weather functionality over single-band face recognition. Thus, in this research, we revisit the hyperspectral recognition problem and provide a deep learning-based approach. A new fusion model (named HyperFace) is proposed to address this problem. The proposed model features a pre-fusion scheme, a Siamese encoder with bi-scope residual dense learning, a feedback-style decoder, and a recognition-oriented composite loss function. Experiments demonstrate that our method yields a much higher recognition rate than face recognition using only visible light or IR data. Moreover, our fusion model is shown to be superior to other general-purpose image fusion methods that are either traditional or deep learning-based, including state-of-the-art methods, in terms of both image quality and recognition performance.
人脸识别在可见光和红外（IR）条件下的光谱内和跨光谱情况下都得到了充分研究。然而，如何融合不同光带进行人脸识别，即高光谱人脸识别，仍然是一个开放的研究问题，与单波段人脸识别相比，它具有信息保留更丰富和具备全天候功能的优势。因此，在本研究中，我们重新审视高光谱识别问题并提供一种基于深度学习的方法。提出了一种新的融合模型（名为HyperFace）来解决这个问题。所提出的模型具有预融合方案、带有双视角残差密集学习的暹罗编码器、反馈式解码器以及面向识别的复合损失函数。实验表明，我们的方法比仅使用可见光或红外数据的人脸识别具有更高的识别率。此外，在图像质量和识别性能方面，我们的融合模型被证明优于其他传统或基于深度学习的通用图像融合方法，包括最先进的方法。
