OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters

• URL: http://arxiv.org/abs/2207.12319v1
• Date: Tue, 19 Jul 2022 17:05:25 GMT
• Title: OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters
• Authors: Piera Riccio and Bill Psomas and Francesco Galati and Francisco Escolano and Thomas Hofmann and Nuria Oliver
• Abstract summary: We present OpenFilter, a framework to apply AR filters available in social media platforms on existing large collections of human faces. We also share FairBeauty and B-LFW, two beautified versions of the publicly available FairFace and LFW datasets.
• Score: 14.789786314914858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Augmented Reality or AR filters on selfies have become very popular on social media platforms for a variety of applications, including marketing, entertainment and aesthetics. Given the wide adoption of AR face filters and the importance of faces in our social structures and relations, there is increased interest by the scientific community to analyze the impact of such filters from a psychological, artistic and sociological perspective. However, there are few quantitative analyses in this area mainly due to a lack of publicly available datasets of facial images with applied AR filters. The proprietary, close nature of most social media platforms does not allow users, scientists and practitioners to access the code and the details of the available AR face filters. Scraping faces from these platforms to collect data is ethically unacceptable and should, therefore, be avoided in research. In this paper, we present OpenFilter, a flexible framework to apply AR filters available in social media platforms on existing large collections of human faces. Moreover, we share FairBeauty and B-LFW, two beautified versions of the publicly available FairFace and LFW datasets and we outline insights derived from the analysis of these beautified datasets.

Related papers

• Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp [13.749279800238092]
  We show that image-text data filtering has biases and is value-laden. Data relating to several imputed demographic groups are associated with higher rates of exclusion. Our conclusions point to a need for fundamental changes in dataset creation and filtering practices.
  arXiv  Detail & Related papers  (2024-05-13T21:53:06Z)
• FaceFilterSense: A Filter-Resistant Face Recognition and Facial Attribute Analysis Framework [1.673834743879962]
  Fun selfie filters have come into tremendous mainstream use affecting the functioning of facial biometric systems. 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. 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.
  arXiv  Detail & Related papers  (2024-04-12T07:04:56Z)
• Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation [30.395376392259497]
  Filter bubbles have been studied extensively within the context of online content platforms. With the rise of short-video platforms, the filter bubble has been given extra attention.
  arXiv  Detail & Related papers  (2024-03-07T14:14:40Z)
• BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering [58.403898834018285]
  BlendFilter is a novel approach that elevates retrieval-augmented Large Language Models by integrating query generation blending with knowledge filtering. We conduct extensive experiments on three open-domain question answering benchmarks, and the findings clearly indicate that our innovative BlendFilter surpasses state-of-the-art baselines significantly.
  arXiv  Detail & Related papers  (2024-02-16T23:28:02Z)
• Multiface: A Dataset for Neural Face Rendering [108.44505415073579]
  In this work, we present Multiface, a new multi-view, high-resolution human face dataset. We introduce Mugsy, a large scale multi-camera apparatus to capture high-resolution synchronized videos of a facial performance. The goal of Multiface is to close the gap in accessibility to high quality data in the academic community and to enable research in VR telepresence.
  arXiv  Detail & Related papers  (2022-07-22T17:55:39Z)
• LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters [53.180678723280145]
  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. Each dataset contains 4,324 images of size 64 x 64, with a total of 34,592 images.
  arXiv  Detail & Related papers  (2022-03-11T17:05:10Z)
• Fun Selfie Filters in Face Recognition: Impact Assessment and Removal [13.715060479044167]
  This work investigates the impact of fun selfie filters on face recognition systems. Ten relevant fun selfie filters are selected to create a database. To mitigate such unwanted effects, a GAN-based selfie filter removal algorithm is proposed.
  arXiv  Detail & Related papers  (2022-02-12T09:12:31Z)
• On the Effect of Selfie Beautification Filters on Face Detection and Recognition [53.561797148529664]
  Social media image filters modify the image contrast or illumination or occlude parts of the face with for example artificial glasses or animal noses. We develop a method to reconstruct the applied manipulation with a modified version of the U-NET segmentation network. From a recognition perspective, we employ distance measures and trained machine learning algorithms applied to features extracted using a ResNet-34 network trained to recognize faces.
  arXiv  Detail & Related papers  (2021-10-17T22:10:56Z)
• A survey on applications of augmented, mixed and virtual reality for nature and environment [114.4879749449579]
  Augmented reality (AR), virtual reality (VR) and mixed reality (MR) are technologies of great potential due to the engaging and enriching experiences they are capable of providing. However, the possibilities that AR, VR and MR offer in the area of environmental applications are not yet widely explored. We present the outcome of a survey meant to discover and classify existing AR/VR/MR applications that can benefit the environment or increase awareness on environmental issues.
  arXiv  Detail & Related papers  (2020-08-27T09:59:27Z)
• DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation [50.08581302050378]
  Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences. We propose a preliminary work of a neural influence diffusion network (i.e., DiffNet) for social recommendation (Diffnet) In this paper, we propose DiffNet++, an improved algorithm of Diffnet that models the neural influence diffusion and interest diffusion in a unified framework.
  arXiv  Detail & Related papers  (2020-01-15T08:45:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.