Fun Selfie Filters in Face Recognition: Impact Assessment and Removal

• URL: http://arxiv.org/abs/2202.06022v1
• Date: Sat, 12 Feb 2022 09:12:31 GMT
• Title: Fun Selfie Filters in Face Recognition: Impact Assessment and Removal
• Authors: Cristian Botezatu, Mathias Ibsen, Christian Rathgeb, Christoph Busch
• Abstract summary: 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.
• Score: 13.715060479044167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• 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)
• Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
  Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
  arXiv  Detail & Related papers  (2023-10-18T14:49:54Z)
• Has the Virtualization of the Face Changed Facial Perception? A Study of the Impact of Photo Editing and Augmented Reality on Facial Perception [7.532782211020641]
  We present the results of six surveys on familiarity with different styles of facial filters and ability to discern whether images are filtered. Our results demonstrate that faces modified with more traditional face filters are perceived similarly to unmodified faces. We discuss possible explanations for these results, including a societal adjustment to traditional photo editing techniques or the inherent differences in the different types of filters.
  arXiv  Detail & Related papers  (2023-03-01T16:09:11Z)
• WSD: Wild Selfie Dataset for Face Recognition in Selfie Images [13.356502206849106]
  We develop Wild Selfie dataset (WSD) where images are captured from selfie cameras of different smart phones. WSD dataset contains 45,424 images from 42 individuals. Average number of images per subject is 1,082 with minimum and maximum number of images for any subject are 518 and 2,634, respectively.
  arXiv  Detail & Related papers  (2023-02-14T18:43:21Z)
• FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders [81.21440457805932]
  We propose a novel framework FaceMAE, where the face privacy and recognition performance are considered simultaneously. randomly masked face images are used to train the reconstruction module in FaceMAE. We also perform sufficient privacy-preserving face recognition on several public face datasets.
  arXiv  Detail & Related papers  (2022-05-23T07:19:42Z)
• 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)
• 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)
• Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario [50.36920272392624]
  Face anti-spoofing is the key to preventing security breaches in biometric recognition applications. Deep learning method using RGB and infrared images demands a large amount of training data for new attacks. We present a face anti-spoofing method in a real-world scenario by automatic learning the physical characteristics in polarization images of a real face.
  arXiv  Detail & Related papers  (2020-03-18T03:04:03Z)
• Towards Face Encryption by Generating Adversarial Identity Masks [53.82211571716117]
  We propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks. TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models.
  arXiv  Detail & Related papers  (2020-03-15T12:45:10Z)

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.