Face Verification with Challenging Imposters and Diversified Demographics

• URL: http://arxiv.org/abs/2110.08667v1
• Date: Sat, 16 Oct 2021 21:34:59 GMT
• Title: Face Verification with Challenging Imposters and Diversified Demographics
• Abstract summary: Face verification aims to distinguish between genuine and imposter pairs of faces, which include the same or different identities, respectively. We introduce the $FaVCI2D$ dataset, which includes metadata related to gender, country and age to facilitate fine-grained analysis of results. Experiments with state-of-the-art deep models that provide nearly 100% performance on existing datasets show a significant performance drop for $FaVCI2D$.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Face verification aims to distinguish between genuine and imposter pairs of faces, which include the same or different identities, respectively. The performance reported in recent years gives the impression that the task is practically solved. Here, we revisit the problem and argue that existing evaluation datasets were built using two oversimplifying design choices. First, the usual identity selection to form imposter pairs is not challenging enough because, in practice, verification is needed to detect challenging imposters. Second, the underlying demographics of existing datasets are often insufficient to account for the wide diversity of facial characteristics of people from across the world. To mitigate these limitations, we introduce the $FaVCI2D$ dataset. Imposter pairs are challenging because they include visually similar faces selected from a large pool of demographically diversified identities. The dataset also includes metadata related to gender, country and age to facilitate fine-grained analysis of results. $FaVCI2D$ is generated from freely distributable resources. Experiments with state-of-the-art deep models that provide nearly 100\% performance on existing datasets show a significant performance drop for $FaVCI2D$, confirming our starting hypothesis. Equally important, we analyze legal and ethical challenges which appeared in recent years and hindered the development of face analysis research. We introduce a series of design choices which address these challenges and make the dataset constitution and usage more sustainable and fairer. $FaVCI2D$ is available at~\url{https://github.com/AIMultimediaLab/FaVCI2D-Face-Verification-with-Challenging-Imposters-and-Diversif ied-Demographics}.

Related papers

• IdentiFace: Multi-Modal Iterative Diffusion Framework for Identifiable Suspect Face Generation in Crime Investigations [9.548761889081904]
  We propose a novel diffusion-based framework for identifiable suspect face generation, called IdentiFace.<n>We show that IdentiFace achieves superior performance over existing methods in terms of identity retrieval and shows strong potential for practical applications.
  arXiv  Detail & Related papers  (2026-05-01T09:04:17Z)
• Designing and Generating Diverse, Equitable Face Image Datasets for Face Verification Tasks [16.06801103297664]
  We propose a comprehensive methodology that integrates advanced generative models to create varied and diverse high-quality synthetic face images.<n>This work not only enriches the discussion on diversity and ethics in artificial intelligence but also lays the foundation for developing more inclusive and reliable face verification technologies.
  arXiv  Detail & Related papers  (2025-11-21T16:53:08Z)
• Hybrid Generative Fusion for Efficient and Privacy-Preserving Face Recognition Dataset Generation [87.48785461212556]
  We present our approach to the DataCV ICCV Challenge, which centers on building a high-quality face dataset to train a face recognition model.<n>The constructed dataset must not contain identities overlapping with any existing public face datasets.<n>Our method achieves textbf1st place in the competition, and experimental results show that our dataset improves model performance across 10K, 20K, and 100K identity scales.
  arXiv  Detail & Related papers  (2025-08-14T14:14:18Z)
• From Large Angles to Consistent Faces: Identity-Preserving Video Generation via Mixture of Facial Experts [69.44297222099175]
  We introduce a Mixture of Facial Experts (MoFE) that captures distinct but mutually reinforcing aspects of facial attributes.<n>To mitigate dataset limitations, we have tailored a data processing pipeline centered on two key aspects: Face Constraints and Identity Consistency.<n>We have curated and refined a Large Face Angles (LFA) dataset from existing open-source human video datasets.
  arXiv  Detail & Related papers  (2025-08-13T04:10:16Z)
• SELFI: Selective Fusion of Identity for Generalizable Deepfake Detection [16.500269508552844]
  Face identity provides a powerful signal for deepfake detection.<n>Some suppress identity cues to reduce bias, while others rely on them as forensic evidence.<n>We propose textbfSELFI (textbfSELective textbfIdentity), a generalizable detection framework that dynamically modulates identity usage.
  arXiv  Detail & Related papers  (2025-06-21T05:11:35Z)
• UniF$^2$ace: A Unified Fine-grained Face Understanding and Generation Model [62.66515621965686]
  We introduce a novel theoretical framework with a Dual Discrete Diffusion (D3Diff) loss, unifying masked generative models with discrete score matching diffusion.<n>This D3Diff significantly enhances the model's ability to synthesize high-fidelity facial details aligned with text input.<n>We construct UniF$2$aceD-1M, a large-scale dataset comprising 130K fine-grained image-caption pairs and 1M visual question-answering pairs.
  arXiv  Detail & Related papers  (2025-03-11T07:34:59Z)
• SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person Re-Identification in Aerial-Ground Networks [24.394237582548918]
  We design and implement the Self-Calibrating and Adaptive Prompt (SeCap) method for the AGPReID task. SeCap relies on the Prompt Re-calibration Module (PRM), which adaptively re-calibrates prompts based on the input. We further contribute two real-world Large-scale Aerial-Ground Person Re-Ground datasets, LAGPeR and G2APS-ReID.
  arXiv  Detail & Related papers  (2025-03-10T06:23:35Z)
• Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification [69.04239222633795]
  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.
  arXiv  Detail & Related papers  (2024-12-04T14:30:19Z)
• ID$^3$: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition [60.15830516741776]
  Synthetic face recognition (SFR) aims to generate datasets that mimic the distribution of real face data. We introduce a diffusion-fueled SFR model termed $textID3$. $textID3$ employs an ID-preserving loss to generate diverse yet identity-consistent facial appearances.
  arXiv  Detail & Related papers  (2024-09-26T06:46:40Z)
• Leveraging Synthetic Data for Generalizable and Fair Facial Action Unit Detection [9.404202619102943]
  We propose to use synthetically generated data and multi-source domain adaptation (MSDA) to address the problems of the scarcity of labeled data and the diversity of subjects. Specifically, we propose to generate a diverse dataset through synthetic facial expression re-targeting. To further improve gender fairness, PM2 matches the features of the real data with a female and a male synthetic image.
  arXiv  Detail & Related papers  (2024-03-15T23:50:18Z)
• Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results [73.98594459933008]
  Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets. We introduce the Wild Face Anti-Spoofing dataset, a large-scale, diverse FAS dataset collected in unconstrained settings.
  arXiv  Detail & Related papers  (2023-04-12T10:29:42Z)
• Learning Feature Recovery Transformer for Occluded Person Re-identification [71.18476220969647]
  We propose a new approach called Feature Recovery Transformer (FRT) to address the two challenges simultaneously. To reduce the interference of the noise during feature matching, we mainly focus on visible regions that appear in both images and develop a visibility graph to calculate the similarity. In terms of the second challenge, based on the developed graph similarity, for each query image, we propose a recovery transformer that exploits the feature sets of its $k$-nearest neighbors in the gallery to recover the complete features.
  arXiv  Detail & Related papers  (2023-01-05T02:36:16Z)
• 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)
• Assessing Demographic Bias Transfer from Dataset to Model: A Case Study in Facial Expression Recognition [1.5340540198612824]
  Two metrics focus on the representational and stereotypical bias of the dataset, and the third one on the residual bias of the trained model. We demonstrate the usefulness of the metrics by applying them to a FER problem based on the popular Affectnet dataset.
  arXiv  Detail & Related papers  (2022-05-20T09:40:42Z)
• Few-shot Partial Multi-view Learning [103.33865779721458]
  We propose a new task called few-shot partial multi-view learning. It focuses on overcoming the negative impact of the view-missing issue in the low-data regime. We conduct extensive experiments to evaluate our method.
  arXiv  Detail & Related papers  (2021-05-05T13:34:43Z)
• Diverse Knowledge Distillation for End-to-End Person Search [81.4926655119318]
  Person search aims to localize and identify a specific person from a gallery of images. Recent methods can be categorized into two groups, i.e., two-step and end-to-end approaches. We propose a simple yet strong end-to-end network with diverse knowledge distillation to break the bottleneck.
  arXiv  Detail & Related papers  (2020-12-21T09:04:27Z)
• Robust Character Labeling in Movie Videos: Data Resources and Self-supervised Feature Adaptation [39.373699774220775]
  We present a dataset of over 169,000 face tracks curated from 240 Hollywood movies with weak labels. We propose an offline algorithm based on nearest-neighbor search in the embedding space to mine hard-examples from these tracks. Overall, we find that multiview correlation-based adaptation yields more discriminative and robust face embeddings.
  arXiv  Detail & Related papers  (2020-08-25T22:07:41Z)
• Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review [79.49390241265337]
  Chalearn Face Anti-spoofing Attack Detection Challenge consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results.
  arXiv  Detail & Related papers  (2020-04-23T06:43:08Z)

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.