Abstract One primary challenge of face recognition is that the performance is seriously affected by varying illumination. Multi-spectral imaging can capture face images in the visible spectrum and beyond, which is deemed to be an effective technology in response to this challenge. For current multi-spectral imaging-based face recognition methods, how to fully explore the discriminant and correlation features from both the intra-spectrum and inter-spectrum aspects with only a limited number of multi-spectral samples for model training has not been well studied. To address this problem, in this paper, we propose a novel face recognition approach named Spectrum-aware Discriminative Deep Learning (SDDL). To take full advantage of the multi-spectral training samples, we build a discriminative multi-spectral network (DMN) and take face sample pairs as the input of the network. By jointly considering the spectrum and the class label information, SDDL trains the network for projecting samples pairs into a discriminant feature subspace, on which the intrinsic relationship including the intra- and inter-spectrum discrimination and the inter-spectrum correlation among face samples is well discovered. The proposed approach is evaluated on three widely used datasets HK PolyU, CMU, and UWA. Extensive experimental results demonstrate the superiority of SDDL over state-of-the-art competing methods.

中文翻译：

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用于多光谱人脸识别的光谱感知判别深度特征学习

摘要 人脸识别的一个主要挑战是性能受到光照变化的严重影响。多光谱成像可以捕捉可见光谱及以上的人脸图像，被认为是应对这一挑战的有效技术。对于目前基于多光谱成像的人脸识别方法，如何利用有限数量的多光谱样本进行模型训练，从谱内和谱间两个方面充分挖掘判别和相关特征还没有得到很好的研究。 . 为了解决这个问题，在本文中，我们提出了一种新的人脸识别方法，称为频谱感知判别深度学习（SDDL）。为了充分利用多光谱训练样本，我们构建了一个判别式多光谱网络（DMN），并将人脸样本对作为网络的输入。通过联合考虑频谱和类标签信息，SDDL训练网络将样本对投影到一个判别特征子空间中，在该子空间上，包括频谱内和频谱间判别以及人脸样本之间的频谱间相关性在内的内在关系很好发现。所提出的方法在三个广泛使用的数据集 HK PolyU、CMU 和 UWA 上进行了评估。大量的实验结果证明了 SDDL 优于最先进的竞争方法。很好地发现了人脸样本之间的谱内和谱间区分以及谱间相关性等内在关系。所提出的方法在三个广泛使用的数据集 HK PolyU、CMU 和 UWA 上进行了评估。大量的实验结果证明了 SDDL 优于最先进的竞争方法。很好地发现了人脸样本之间的谱内和谱间区分以及谱间相关性等内在关系。所提出的方法在三个广泛使用的数据集 HK PolyU、CMU 和 UWA 上进行了评估。大量的实验结果证明了 SDDL 优于最先进的竞争方法。