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NIR and Visible Image Fusion for Improving Face Recognition at Long Distance

机译：NIR和可见图像融合，用于改善长距离的人脸识别

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Face recognition performance achieves high accuracy in close proximity. However, great challenges still exist in recognizing human face at long distance. In fact, the rapidly increasing need for long range surveillance requires a passage from close-up distances to long distances which affects strongly the human face image quality and causes degradation in recognition accuracy. To address this problem, we propose in this paper, a multispectral pixel level fusion approach to improve the performance of automatic face recognition at long distance. The main objective of the proposed approach is to formulate a method to enhance the face image quality as well as the face recognition rate. First, visible and near-infrared images are decomposed into a different bands using discrete wavelet transform. Then, the fusion process is performed through the singular value decomposition and principal component analysis. The results highlight further the still challenging problem of face recognition at long distance, as well as the effectiveness of our proposed approach as an alternative solution to this problem.
机译：面部识别性能在近距离接近实现高精度。然而，在长途识别人脸时仍然存在巨大挑战。事实上，对长距离监测的快速增加需要从特写距离到长距离的通道，这影响强烈的人类面部图像质量并导致识别准确性的降级。为了解决这个问题，我们提出了一种多光谱像素电平融合方法，可以在长距离改善自动面部识别的性能。所提出的方法的主要目的是制定一种提高面部图像质量以及面部识别率的方法。首先，可见和近红外图像使用离散小波变换分解成不同的频带。然后，通过奇异值分解和主成分分析来执行融合过程。结果突出了长距离人物识别的仍然具有挑战性的问题，以及我们所提出的方法作为解决此问题的替代解决方案的有效性。

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