Synthetic Counterfactual Faces

• URL: http://arxiv.org/abs/2407.13922v2
• Date: Mon, 29 Jul 2024 18:29:50 GMT
• Title: Synthetic Counterfactual Faces
• Authors: Guruprasad V Ramesh, Harrison Rosenberg, Ashish Hooda, Shimaa Ahmed Kassem Fawaz,
• Abstract summary: We build a generative AI framework to construct targeted, counterfactual, high-quality synthetic face data. Our pipeline has many use cases, including face recognition systems sensitivity evaluations and image understanding system probes. We showcase the efficacy of our face generation pipeline on a leading commercial vision model.
• Score: 1.3062016289815055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computer vision systems have been deployed in various applications involving biometrics like human faces. These systems can identify social media users, search for missing persons, and verify identity of individuals. While computer vision models are often evaluated for accuracy on available benchmarks, more annotated data is necessary to learn about their robustness and fairness against semantic distributional shifts in input data, especially in face data. Among annotated data, counterfactual examples grant strong explainability characteristics. Because collecting natural face data is prohibitively expensive, we put forth a generative AI-based framework to construct targeted, counterfactual, high-quality synthetic face data. Our synthetic data pipeline has many use cases, including face recognition systems sensitivity evaluations and image understanding system probes. The pipeline is validated with multiple user studies. We showcase the efficacy of our face generation pipeline on a leading commercial vision model. We identify facial attributes that cause vision systems to fail.

Related papers

• SIG: A Synthetic Identity Generation Pipeline for Generating Evaluation Datasets for Face Recognition [0.0]
  We introduce the Synthetic Identity Generation pipeline, or SIG, that allows for the targeted creation of ethical, balanced datasets for face recognition evaluation. Our pipeline generates high-quality images of synthetic identities with controllable pose, facial features, and demographic attributes, such as race, gender, and age. We also release an open-source evaluation dataset named ControlFace10k, consisting of 10,008 face images of 3,336 unique synthetic identities balanced across race, gender, and age.
  arXiv  Detail & Related papers  (2024-09-12T18:18:02Z)
• 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)
• 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)
• Face Recognition Using Synthetic Face Data [0.0]
  We highlight the promising application of synthetic data, generated through rendering digital faces via our computer graphics pipeline, in achieving competitive results. By finetuning the model,we obtain results that rival those achieved when training with hundreds of thousands of real images. We also investigate the contribution of adding intra-class variance factors (e.g., makeup, accessories, haircuts) on model performance.
  arXiv  Detail & Related papers  (2023-05-17T09:26:10Z)
• Robustness Disparities in Face Detection [64.71318433419636]
  We present the first of its kind detailed benchmark of face detection systems, specifically examining the robustness to noise of commercial and academic models. Across all the datasets and systems, we generally find that photos of individuals who are $textitmasculine presenting$, of $textitolder$, of $textitdarker skin type$, or have $textitdim lighting$ are more susceptible to errors than their counterparts in other identities.
  arXiv  Detail & Related papers  (2022-11-29T05:22:47Z)
• CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition [80.07590100872548]
  We propose Contrastive Inhibitory Adaptati On (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets. CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations.
  arXiv  Detail & Related papers  (2022-08-10T15:46:05Z)
• Fairness Indicators for Systematic Assessments of Visual Feature Extractors [21.141633753573764]
  We propose three fairness indicators, which aim at quantifying harms and biases of visual systems. Our indicators use existing publicly available datasets collected for fairness evaluations. These indicators are not intended to be a substitute for a thorough analysis of the broader impact of the new computer vision technologies.
  arXiv  Detail & Related papers  (2022-02-15T17:45:33Z)
• Evaluation of Human and Machine Face Detection using a Novel Distinctive Human Appearance Dataset [0.76146285961466]
  We evaluate current state-of-the-art face-detection models in their ability to detect faces in images. The evaluation results show that face-detection algorithms do not generalize well to diverse appearances.
  arXiv  Detail & Related papers  (2021-11-01T02:20:40Z)
• Robustness Disparities in Commercial Face Detection [72.25318723264215]
  We present the first of its kind detailed benchmark of the robustness of three such systems: Amazon Rekognition, Microsoft Azure, and Google Cloud Platform. We generally find that photos of individuals who are older, masculine presenting, of darker skin type, or have dim lighting are more susceptible to errors than their counterparts in other identities.
  arXiv  Detail & Related papers  (2021-08-27T21:37:16Z)
• SynFace: Face Recognition with Synthetic Data [83.15838126703719]
  We devise the SynFace with identity mixup (IM) and domain mixup (DM) to mitigate the performance gap. We also perform a systematically empirical analysis on synthetic face images to provide some insights on how to effectively utilize synthetic data for face recognition.
  arXiv  Detail & Related papers  (2021-08-18T03:41:54Z)
• A high fidelity synthetic face framework for computer vision [10.679578971210912]
  We propose synthesizing facial data, including ground truth annotations, at the consistency and scale possible through use of synthetic data. We use a parametric face model together with hand crafted assets which enable us to generate training data with unprecedented quality and diversity.
  arXiv  Detail & Related papers  (2020-07-16T14:40:28Z)

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.