Rectilinear face recognition models suffer from severe performance degradation when applied to fisheye images captured by 360° back-to-back dual fisheye cameras. We propose a novel face rectification method to combat the effect of fisheye image distortion on face recognition. The method consists of a classification network and a restoration network specifically designed to handle the non-linear property of fisheye projection. The classification network classifies an input fisheye image according to its distortion level. The restoration network takes a distorted image as input and restores the rectilinear geometric structure of the face. The performance of the proposed method is tested on an end-to-end face recognition system constructed by integrating the proposed rectification method with a conventional rectilinear face recognition system. The face verification accuracy of the integrated system is 99.18% when tested on images in the synthetic Labeled Faces in the Wild (LFW) dataset and 95.70% for images in a real image dataset, resulting in an average accuracy improvement of 6.57% over the conventional face recognition system. For face identification, the average improvement over the conventional face recognition system is 4.51%.

中文翻译：

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360°双鱼眼镜头的深脸矫正。

直线人脸识别模型应用于360°背靠背双鱼眼相机拍摄的鱼眼图像时，性能会严重下降。我们提出了一种新颖的人脸矫正方法来对抗鱼眼图像失真对人脸识别的影响。该方法包括一个分类网络和一个专为处理鱼眼投影的非线性特性而设计的恢复网络。分类网络根据输入的鱼眼图像的失真等级对它进行分类。复原网络将失真的图像作为输入，并复原脸部的直线几何结构。该方法的性能在端到端人脸识别系统上进行了测试，该端对端人脸识别系统是通过将所提出的整流方法与常规直线人脸识别系统集成而构建的。当在Wild（LFW）数据集中的合成标记面部图像上进行测试时，集成系统的面部验证精度为99.18％，对于真实图像数据集中的图像进行测试时为95.70％，与传统方法相比，平均准确性提高了6.57％。人脸识别系统。对于人脸识别，与传统人脸识别系统相比，平均改善率为4.51％。