论文标题
有趣的自拍照过滤器面部识别：影响评估和删除
Fun Selfie Filters in Face Recognition: Impact Assessment and Removal
论文作者
论文摘要
这项工作调查了有趣的自拍照过滤器的影响，这些过滤器经常用于修改自拍照对面部识别系统。基于对自由可用移动应用程序的定性评估和分类，选择了十个相关的自拍过滤器来创建数据库。为此，所选的过滤器会自动应用于面对公共面部图像数据库的图像。不同的最新方法用于评估使用DLIB，视网膜面和COTS方法的有趣自拍过滤器对面部检测性能的影响，由FaceQnet和磁磁带估算的样品质量以及采用Arcface和COTS算法的识别精度。获得的结果表明，自拍照过滤器会对面部识别模块产生负面影响，尤其是如果有趣的自拍照过滤器覆盖了脸部的大部分区域，遮盖了嘴，鼻子和眼睛的大部分。为了减轻这种不必要的效果，提出了一种基于GAN的自拍过滤器去除算法，该算法由分割模块，感知网络和一代模块组成。在跨数据库实验中，提出的自拍滤清器去除技术的应用已显示出显着改善基础面部识别系统的生物识别性能。
This work investigates the impact of fun selfie filters, which are frequently used to modify selfies, on face recognition systems. Based on a qualitative assessment and classification of freely available mobile applications, ten relevant fun selfie filters are selected to create a database. To this end, the selected filters are automatically applied to face images of public face image databases. Different state-of-the-art methods are used to evaluate the influence of fun selfie filters on the performance of face detection using dlib, RetinaFace, and a COTS method, sample quality estimated by FaceQNet and MagFace, and recognition accuracy employing ArcFace and a COTS algorithm. The obtained results indicate that selfie filters negatively affect face recognition modules, especially if fun selfie filters cover a large region of the face, where the mouth, nose, and eyes are covered. To mitigate such unwanted effects, a GAN-based selfie filter removal algorithm is proposed which consists of a segmentation module, a perceptual network, and a generation module. In a cross-database experiment the application of the presented selfie filter removal technique has shown to significantly improve the biometric performance of the underlying face recognition systems.
