Toward Fairer Face Recognition Datasets

• URL: http://arxiv.org/abs/2406.16592v3
• Date: Wed, 23 Oct 2024 06:45:20 GMT
• Title: Toward Fairer Face Recognition Datasets
• Authors: Alexandre Fournier-Montgieux, Michael Soumm, Adrian Popescu, Bertrand Luvison, Hervé Le Borgne,
• Abstract summary: 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.
• Score: 69.04239222633795
• License:
• Abstract: Face recognition and verification are two computer vision tasks whose performance has progressed with the introduction of deep representations. However, ethical, legal, and technical challenges due to the sensitive character of face data and biases in real training datasets hinder their development. Generative AI addresses privacy by creating fictitious identities, but fairness problems persist. We promote fairness by introducing a demographic attributes balancing mechanism in generated training datasets. We experiment with an existing real dataset, three generated training datasets, and the balanced versions of a diffusion-based dataset. We propose a comprehensive evaluation that considers accuracy and fairness equally and includes a rigorous regression-based statistical analysis of attributes. The analysis shows that balancing reduces demographic unfairness. Also, a performance gap persists despite generation becoming more accurate with time. The proposed balancing method and comprehensive verification evaluation promote fairer and transparent face recognition and verification.

Related papers

• Balancing the Scales: Enhancing Fairness in Facial Expression Recognition with Latent Alignment [5.784550537553534]
  This workleverages representation learning based on latent spaces to mitigate bias in facial expression recognition systems. It also enhances a deep learning model's fairness and overall accuracy.
  arXiv  Detail & Related papers  (2024-10-25T10:03:10Z)
• The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition [10.849598219674132]
  We investigate the impact of demographically balanced authentic and synthetic data, both individually and in combination, on the accuracy and fairness of face recognition models. Our findings emphasize two main points: (i) the increased effectiveness of training data generated by diffusion-based models in enhancing accuracy, whether used alone or combined with subsets of authentic data, and (ii) the minimal impact of incorporating balanced data from pre-trained generative methods on fairness.
  arXiv  Detail & Related papers  (2024-09-04T16:50:48Z)
• SDFR: Synthetic Data for Face Recognition Competition [51.9134406629509]
  Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. Recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones.
  arXiv  Detail & Related papers  (2024-04-06T10:30:31Z)
• Learning for Counterfactual Fairness from Observational Data [62.43249746968616]
  Fairness-aware machine learning aims to eliminate biases of learning models against certain subgroups described by certain protected (sensitive) attributes such as race, gender, and age. A prerequisite for existing methods to achieve counterfactual fairness is the prior human knowledge of the causal model for the data. In this work, we address the problem of counterfactually fair prediction from observational data without given causal models by proposing a novel framework CLAIRE.
  arXiv  Detail & Related papers  (2023-07-17T04:08:29Z)
• Model Debiasing via Gradient-based Explanation on Representation [14.673988027271388]
  We propose a novel fairness framework that performs debiasing with regard to sensitive attributes and proxy attributes. Our framework achieves better fairness-accuracy trade-off on unstructured and structured datasets than previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-05-20T11:57:57Z)
• What Should Be Balanced in a "Balanced" Face Recognition Dataset? [8.820019122897154]
  Various face image datasets have been proposed as 'fair' or 'balanced' to assess the accuracy of face recognition algorithms across demographics. It is important to note that the number of identities and images in an evaluation dataset are em not driving factors for 1-to-1 face matching accuracy. We propose a bias-aware toolkit that facilitates creation of cross-demographic evaluation datasets balanced on factors mentioned in this paper.
  arXiv  Detail & Related papers  (2023-04-17T22:02:03Z)
• Attention-based Adversarial Appearance Learning of Augmented Pedestrians [49.25430012369125]
  We propose a method to synthesize realistic data for the pedestrian recognition task. Our approach utilizes an attention mechanism driven by an adversarial loss to learn domain discrepancies. Our experiments confirm that the proposed adaptation method is robust to such discrepancies and reveals both visual realism and semantic consistency.
  arXiv  Detail & Related papers  (2021-07-06T15:27:00Z)
• Representative & Fair Synthetic Data [68.8204255655161]
  We present a framework to incorporate fairness constraints into the self-supervised learning process. We generate a representative as well as fair version of the UCI Adult census data set. We consider representative & fair synthetic data a promising future building block to teach algorithms not on historic worlds, but rather on the worlds that we strive to live in.
  arXiv  Detail & Related papers  (2021-04-07T09:19:46Z)
• Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination [53.3082498402884]
  A growing specter in the rise of machine learning is whether the decisions made by machine learning models are fair. We present a framework of fair semi-supervised learning in the pre-processing phase, including pseudo labeling to predict labels for unlabeled data. A theoretical decomposition analysis of bias, variance and noise highlights the different sources of discrimination and the impact they have on fairness in semi-supervised learning.
  arXiv  Detail & Related papers  (2020-09-25T05:48:56Z)
• Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing [15.689539491203373]
  Machine learning fairness concerns about the biases towards certain protected or sensitive group of people when addressing the target tasks. This paper studies the debiasing problem in the context of image classification tasks.
  arXiv  Detail & Related papers  (2020-07-27T15:17:52Z)

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.