With the advent of social media, fun selfie filters have come into tremendous
mainstream use affecting the functioning of facial biometric systems as well as
image recognition systems. These filters vary from beautification filters and
Augmented Reality (AR)-based filters to filters that modify facial landmarks.
Hence, there is a need to assess the impact of such filters on the performance
of existing face recognition systems. The limitation associated with existing
solutions is that these solutions focus more on the beautification filters.
However, the current AR-based filters and filters which distort facial key
points are in vogue recently and make the faces highly unrecognizable even to
the naked eye. Also, the filters considered are mostly obsolete with limited
variations. To mitigate these limitations, we aim to perform a holistic impact
analysis of the latest filters and propose an user recognition model with the
filtered images. We have utilized a benchmark dataset for baseline images, and
applied the latest filters over them to generate a beautified/filtered dataset.
Next, we have introduced a model FaceFilterNet for beautified user recognition.
In this framework, we also utilize our model to comment on various attributes
of the person including age, gender, and ethnicity. In addition, we have also
presented a filter-wise impact analysis on face recognition, age estimation,
gender, and ethnicity prediction. The proposed method affirms the efficacy of
our dataset with an accuracy of 87.25% and an optimal accuracy for facial
attribute analysis.

社交媒体上流行的自拍滤镜对面部生物识别系统和图像识别系统的功能产生了巨大影响，本研究旨在综合评估最新滤镜对现有人脸识别系统性能的影响，并提出了一种基于滤镜图像的用户识别模型。

FaceFilterSense：一种抵抗滤镜的面部识别和面部属性分析框架

FaceFilterSense: A Filter-Resistant Face Recognition and Facial  Attribute Analysis Framework

The development of facial biometric systems has contributed greatly to the
development of the computer vision field. Nowadays, there's always a need to
develop a multimodal system that combines multiple biometric traits in an
efficient, meaningful way. In this paper, we introduce "IdentiFace" which is a
multimodal facial biometric system that combines the core of facial recognition
with some of the most important soft biometric traits such as gender, face
shape, and emotion. We also focused on developing the system using only VGG-16
inspired architecture with minor changes across different subsystems. This
unification allows for simpler integration across modalities. It makes it
easier to interpret the learned features between the tasks which gives a good
indication about the decision-making process across the facial modalities and
potential connection. For the recognition problem, we acquired a 99.2% test
accuracy for five classes with high intra-class variations using data collected
from the FERET database[1]. We achieved 99.4% on our dataset and 95.15% on the
public dataset[2] in the gender recognition problem. We were also able to
achieve a testing accuracy of 88.03% in the face-shape problem using the
celebrity face-shape dataset[3]. Finally, we achieved a decent testing accuracy
of 66.13% in the emotion task which is considered a very acceptable accuracy
compared to related work on the FER2013 dataset[4].

IdentiFace 是一个多模式人脸生物特征系统，通过使用 VGG-16 模型和不同子系统的微小改进，将面部识别与性别、面部形状和情感等重要的软生物特征相结合，实现了五个类别的测试精度达到 99.2％，性别识别、面部形状和情感识别问题也取得了不错的测试精度。