利用可见光+近红外信息进行CNN人脸识别

Sanae Boutarfass, B. Besserer
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引用次数: 2

摘要

基本上,由于传感器对近红外光谱很敏感,每个数码相机都可以获取扩展可见光谱的信息——这有时只需要对设备进行微小的修改。因此,使用传统的数码相机并剥离内部ICF(红外截止滤波器)滤波器,我们使用捕获的可见光+近红外图像(也称为全光谱或VNIR图像)来解决经典的人脸识别问题。相机将图像存储为3通道RGB文件,用这些全光谱图像训练和评估CNN会得到令人惊讶的好结果。相反,用RGB+NIR(4通道)做同样的事情不会表现得很好。研究表明,蓝色通道对该任务的贡献较弱,在通道中加入近红外,增加信息,提高信噪比,特别是在蓝色通道中,识别率显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Visible+NIR information for CNN face recognition
Basically every digital camera can acquire information that extends the visible spectrum since the sensor is sensitive to the near-infrared spectrum - and this sometimes requires only minor modifications to the device. So, using a conventional digital camera and stripping off the internal ICF (Infrared Cut-off Filter) filter, we use the captured Visible + NIR images (also called full-spectrum or VNIR images) for the classical face recognition problem. The camera stores the image as 3 channels RGB files, and training and evaluating a CNN with these full-spectrum images lead to surprisingly good results. On the contrary, doing the same with RGB+NIR (4 channels) does not perform as well. The paper shows that the contribution of the blue channel to this task is weak, and the recognition rate raises significantly when NIR is added to the channels, adding information and increasing signal to noise ratio especially in the blue channel.
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