论文标题
使用深度自动编码器的热量到可见的面部识别
Thermal to Visible Face Recognition Using Deep Autoencoders
论文作者
论文摘要
可见的面部识别系统使用深度学习获得了几乎完美的识别精度。但是，在缺乏光线下，这些系统的性能很差。解决此问题的一种方法是热能到可见的跨域面部匹配。这是一项理想的技术，因为它在夜间监视中有用。然而，由于两个领域之间的差异，这是一个非常具有挑战性的面部识别问题。在本文中，我们提出了一个基于深度自动编码器的系统，以了解可见面和热面图像之间的映射。另外，我们评估了热识别到可见面部识别的对齐的影响。为此，我们手动注释了Carl和Eurecom数据集上的面部地标。在三个公开可用的数据集上对拟议的方法进行了广泛的测试：CARL，UND-X1和EURECOM。实验结果表明，所提出的方法可显着改善最新方法。我们观察到，对齐将使性能提高约2％。可以从以下链接下载带注释的面部标志性位置：github.com/alpkant/thermal-to-visible-face-face-rencognition-using-deep-autoencoders。
Visible face recognition systems achieve nearly perfect recognition accuracies using deep learning. However, in lack of light, these systems perform poorly. A way to deal with this problem is thermal to visible cross-domain face matching. This is a desired technology because of its usefulness in night time surveillance. Nevertheless, due to differences between two domains, it is a very challenging face recognition problem. In this paper, we present a deep autoencoder based system to learn the mapping between visible and thermal face images. Also, we assess the impact of alignment in thermal to visible face recognition. For this purpose, we manually annotate the facial landmarks on the Carl and EURECOM datasets. The proposed approach is extensively tested on three publicly available datasets: Carl, UND-X1, and EURECOM. Experimental results show that the proposed approach improves the state-of-the-art significantly. We observe that alignment increases the performance by around 2%. Annotated facial landmark positions in this study can be downloaded from the following link: github.com/Alpkant/Thermal-to-Visible-Face-Recognition-Using-Deep-Autoencoders .
