IdentiFace: Multi-Modal Iterative Diffusion Framework for Identifiable Suspect Face Generation in Crime Investigations

• URL: http://arxiv.org/abs/2605.00526v1
• Date: Fri, 01 May 2026 09:04:17 GMT
• Title: IdentiFace: Multi-Modal Iterative Diffusion Framework for Identifiable Suspect Face Generation in Crime Investigations
• Abstract summary: 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.
• Score: 9.548761889081904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Suspect face generation remains a technical challenge in crime investigations. Traditional sketch-drawing workflows suffer from low efficiency and quality, while diffusion-based approaches still face intrinsic limitations on conditional ambiguity for text-to-image models and sampling variance for one-shot generation. We proposed IdentiFace, a novel diffusion-based framework for identifiable suspect face generation, which addressed these issues through (1) multi-modal input design to strengthen conditional control, and (2) an iterative generation pipeline enabling identifiable feature adjustment. We additionally contributed a facial identity loss and two task-specific datasets. Comprehensive experiments on synthetic datasets and in real-world scenarios indicate that IdentiFace achieves superior performance over existing methods, especially in terms of identity retrieval, and shows strong potential for practical applications.

Related papers

• SteerFace: Debiasing Synthetic Face Generation via Adaptive Residue Perturbation [24.834863849265034]
  This paper identifies visual tendency as a previously underexplored limitation, whereby synthetic data exhibit an unrealistic prevalence of visual attributes.<n>Visual tendency can be attributed to the generator's conditioning on identity embeddings, through which co-occurring residual visual cues are unintentionally absorbed into learned identity semantics.<n>To discourage the generator from exploiting such visual cues, this paper proposes SteerFace, a simple and efficient training framework that perturbs identity embeddings by steering them toward random directions on the embedding hypersphere.
  arXiv  Detail & Related papers  (2026-05-29T06:29:07Z)
• Face inpainting with Identity Preserving Latent Diffusion Models [10.348748950921667]
  ID-ControlNet is an identity-preserving face inpainting framework built upon latent diffusion models.<n>We introduce an identity consistency and triplet loss training strategy that explicitly enforces alignment between the generated face and the target identity representation.
  arXiv  Detail & Related papers  (2026-05-15T23:19:43Z)
• Optimizing ID Consistency in Multimodal Large Models: Facial Restoration via Alignment, Entanglement, and Disentanglement [54.199726425201895]
  Multimodal editing large models have demonstrated powerful editing capabilities across diverse tasks.<n>Current facial ID preservation methods struggle to achieve consistent restoration of both facial identity and edited element IP.<n>We propose EditedID, an Alignment-Disentanglement-Entanglement framework for robust identity-specific facial restoration.
  arXiv  Detail & Related papers  (2026-02-21T08:24:42Z)
• UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition [42.86969216015855]
  Face recognition (FR) stands as one of the most crucial applications in computer vision.<n>We propose a framework to enhance intra-class diversity for synthetic face recognition, shortened as UIFace.<n> Experiments show that our method significantly surpasses previous approaches with even less training data and half the size of synthetic dataset.
  arXiv  Detail & Related papers  (2025-02-27T06:22:18Z)
• OSDFace: One-Step Diffusion Model for Face Restoration [72.5045389847792]
  Diffusion models have demonstrated impressive performance in face restoration.<n>We propose OSDFace, a novel one-step diffusion model for face restoration.<n>Results demonstrate that OSDFace surpasses current state-of-the-art (SOTA) methods in both visual quality and quantitative metrics.
  arXiv  Detail & Related papers  (2024-11-26T07:07:48Z)
• 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)
• 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.<n>We propose an Anomalous cue Guided FAS (AG-FAS) method, which can effectively leverage large-scale additional real faces.<n>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)
• COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
  Face forgery recognition methods can only process one face at a time. Most face forgery recognition methods can only process one face at a time. We propose COMICS, an end-to-end framework for multi-face forgery detection.
  arXiv  Detail & Related papers  (2023-08-03T03:37:13Z)
• Controllable Inversion of Black-Box Face Recognition Models via Diffusion [8.620807177029892]
  We tackle the task of inverting the latent space of pre-trained face recognition models without full model access. We show that the conditional diffusion model loss naturally emerges and that we can effectively sample from the inverse distribution. Our method is the first black-box face recognition model inversion method that offers intuitive control over the generation process.
  arXiv  Detail & Related papers  (2023-03-23T03:02:09Z)

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.