论文标题
LFW-Beautified：带有美化和增强现实过滤器的面部图像的数据集
LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters
论文作者
论文摘要
自拍图像在社交媒体上享有广泛的知名度。以共享此类图像为中心的相同平台提供了过滤器来美化它们或结合了增强现实效果。研究表明，过滤的图像吸引了更多的观点和参与度。自拍照图像也在越来越多地在安全应用程序中使用，因为手机成为许多交易的数据中心。此外，大流行期间蓬勃发展的视频会议申请包括此类过滤器。 这种过滤器可能会破坏允许人识别甚至检测面部本身的生物特征特征，即使不一定使用此类商品应用来损害面部系统。这也可能影响随后的调查，例如社交媒体中的犯罪，在社交媒体中通常需要自动分析，因为在社交网站上发布的信息量或存储在设备或云存储库中的信息。 为了帮助解决此类问题，我们用包括几种操纵的面部图像数据库做出了贡献。它包括图像增强过滤器（主要是修改对比度和闪电）以及增强现实过滤器，这些过滤器结合了动物鼻子或眼镜等物品。此外，用太阳镜的图像使用经过训练的重建网络处理，以学会扭转这种修改。这是因为在文献中观察到对眼部区域的混淆，对面部检测或识别的准确性具有最大的影响。 我们从野生（LFW）数据库中流行的标签面开始，我们对其应用不同的修改，生成8个数据集。每个数据集包含4,324张尺寸64 x 64的图像，共34,592张图像。使用公共和广泛使用的面部数据集可以进行复制和比较。 创建的数据库可在https://github.com/halmstaduniversitybiometrics/lfw-beautified中找到
Selfie images enjoy huge popularity in social media. The same platforms centered around sharing this type of images offer filters to beautify them or incorporate augmented reality effects. Studies suggests that filtered images attract more views and engagement. Selfie images are also in increasing use in security applications due to mobiles becoming data hubs for many transactions. Also, video conference applications, boomed during the pandemic, include such filters. Such filters may destroy biometric features that would allow person recognition or even detection of the face itself, even if such commodity applications are not necessarily used to compromise facial systems. This could also affect subsequent investigations like crimes in social media, where automatic analysis is usually necessary given the amount of information posted in social sites or stored in devices or cloud repositories. To help in counteracting such issues, we contribute with a database of facial images that includes several manipulations. It includes image enhancement filters (which mostly modify contrast and lightning) and augmented reality filters that incorporate items like animal noses or glasses. Additionally, images with sunglasses are processed with a reconstruction network trained to learn to reverse such modifications. This is because obfuscating the eye region has been observed in the literature to have the highest impact on the accuracy of face detection or recognition. We start from the popular Labeled Faces in the Wild (LFW) database, to which we apply different modifications, generating 8 datasets. Each dataset contains 4,324 images of size 64 x 64, with a total of 34,592 images. The use of a public and widely employed face dataset allows for replication and comparison. The created database is available at https://github.com/HalmstadUniversityBiometrics/LFW-Beautified
