Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification

• URL: http://arxiv.org/abs/2412.03349v1
• Date: Wed, 04 Dec 2024 14:30:19 GMT
• Title: Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification
• Authors: Alexandre Fournier-Montgieux, Michael Soumm, Adrian Popescu, Bertrand Luvison, Hervé Le Borgne,
• Abstract summary: Face recognition and verification are two computer vision tasks whose performances have advanced with the introduction of deep representations. Ethical, legal, and technical challenges due to the sensitive nature of face data and biases in real-world training datasets hinder their development. We introduce a new controlled generation pipeline that improves fairness.
• Score: 69.04239222633795
• License:
• Abstract: Face recognition and verification are two computer vision tasks whose performances have advanced with the introduction of deep representations. However, ethical, legal, and technical challenges due to the sensitive nature of face data and biases in real-world training datasets hinder their development. Generative AI addresses privacy by creating fictitious identities, but fairness problems remain. Using the existing DCFace SOTA framework, we introduce a new controlled generation pipeline that improves fairness. Through classical fairness metrics and a proposed in-depth statistical analysis based on logit models and ANOVA, we show that our generation pipeline improves fairness more than other bias mitigation approaches while slightly improving raw performance.

Related papers

• Toward Fairer Face Recognition Datasets [69.04239222633795]
  Face recognition and verification are computer vision tasks whose performance has progressed with the introduction of deep representations. Ethical, legal, and technical challenges due to the sensitive character of face data and biases in real training datasets hinder their development. We promote fairness by introducing a demographic attributes balancing mechanism in generated training datasets.
  arXiv  Detail & Related papers  (2024-06-24T12:33:21Z)
• AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark [12.368133562194267]
  We introduce the AI-Face dataset, the first million-scale demographically annotated AI-generated face image dataset. Based on this dataset, we conduct the first comprehensive fairness benchmark to assess various AI face detectors.
  arXiv  Detail & Related papers  (2024-06-02T15:51:33Z)
• DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis [71.40724659748787]
  DiffusionFace is the first diffusion-based face forgery dataset. It covers various forgery categories, including unconditional and Text Guide facial image generation, Img2Img, Inpaint, and Diffusion-based facial exchange algorithms. It provides essential metadata and a real-world internet-sourced forgery facial image dataset for evaluation.
  arXiv  Detail & Related papers  (2024-03-27T11:32:44Z)
• Generalized Face Liveness Detection via De-fake Face Generator [52.23271636362843]
  Previous Face Anti-spoofing (FAS) methods face the challenge of generalizing to unseen domains. We propose an Anomalous cue Guided FAS (AG-FAS) method, which can effectively leverage large-scale additional real faces. Our method achieves state-of-the-art results under cross-domain evaluations with unseen scenarios and unknown presentation attacks.
  arXiv  Detail & Related papers  (2024-01-17T06:59:32Z)
• My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymization [4.725675279167593]
  We introduce three face access models in a hypothetical social network, where the user has the power to only appear in photos they approve. Our approach eclipses current tagging systems and replaces unapproved faces with quantitatively dissimilar deepfakes. Running seven SOTA face recognizers on our results, MFMC reduces the average accuracy by 61%.
  arXiv  Detail & Related papers  (2022-11-02T17:58:20Z)
• Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition [107.58227666024791]
  Face recognition systems are widely deployed in safety-critical applications, including law enforcement. They exhibit bias across a range of socio-demographic dimensions, such as gender and race. Previous works on bias mitigation largely focused on pre-processing the training data.
  arXiv  Detail & Related papers  (2022-10-18T15:46:05Z)
• Robust Attentive Deep Neural Network for Exposing GAN-generated Faces [40.15016121723183]
  We propose a robust, attentive, end-to-end network that can spot GAN-generated faces by analyzing their eye inconsistencies. Our deep network addresses the imbalance learning issues by considering the AUC loss and the traditional cross-entropy loss jointly.
  arXiv  Detail & Related papers  (2021-09-05T21:22:39Z)
• Dual-Attention GAN for Large-Pose Face Frontalization [59.689836951934694]
  We present a novel Dual-Attention Generative Adversarial Network (DA-GAN) for photo-realistic face frontalization. Specifically, a self-attention-based generator is introduced to integrate local features with their long-range dependencies. A novel face-attention-based discriminator is applied to emphasize local features of face regions.
  arXiv  Detail & Related papers  (2020-02-17T20:00:56Z)

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.