Vec2Face: Scaling Face Dataset Generation with Loosely Constrained Vectors

• URL: http://arxiv.org/abs/2409.02979v4
• Date: Thu, 06 Feb 2025 23:53:15 GMT
• Title: Vec2Face: Scaling Face Dataset Generation with Loosely Constrained Vectors
• Authors: Haiyu Wu, Jaskirat Singh, Sicong Tian, Liang Zheng, Kevin W. Bowyer,
• Abstract summary: Vec2Face is a holistic model that uses only a sampled vector as input. Vec2Face generates as many as 300K identities. Vec2Face achieves state-of-the-art accuracy, from 92% to 93.52%, on five real-world test sets.
• Score: 19.02273216268032
• License:
• Abstract: This paper studies how to synthesize face images of non-existent persons, to create a dataset that allows effective training of face recognition (FR) models. Besides generating realistic face images, two other important goals are: 1) the ability to generate a large number of distinct identities (inter-class separation), and 2) a proper variation in appearance of the images for each identity (intra-class variation). However, existing works 1) are typically limited in how many well-separated identities can be generated and 2) either neglect or use an external model for attribute augmentation. We propose Vec2Face, a holistic model that uses only a sampled vector as input and can flexibly generate and control the identity of face images and their attributes. Composed of a feature masked autoencoder and an image decoder, Vec2Face is supervised by face image reconstruction and can be conveniently used in inference. Using vectors with low similarity among themselves as inputs, Vec2Face generates well-separated identities. Randomly perturbing an input identity vector within a small range allows Vec2Face to generate faces of the same identity with proper variation in face attributes. It is also possible to generate images with designated attributes by adjusting vector values with a gradient descent method. Vec2Face has efficiently synthesized as many as 300K identities, whereas 60K is the largest number of identities created in the previous works. As for performance, FR models trained with the generated HSFace datasets, from 10k to 300k identities, achieve state-of-the-art accuracy, from 92% to 93.52%, on five real-world test sets (\emph{i.e.}, LFW, CFP-FP, AgeDB-30, CALFW, and CPLFW). For the first time, the FR model trained using our synthetic training set achieves higher accuracy than that trained using a same-scale training set of real face images on the CALFW, IJBB, and IJBC test sets.

Related papers

• Turn That Frown Upside Down: FaceID Customization via Cross-Training Data [49.51940625552275]
  CrossFaceID is the first large-scale, high-quality, and publicly available dataset designed to improve the facial modification capabilities of FaceID customization models. It consists of 40,000 text-image pairs from approximately 2,000 persons, with each person represented by around 20 images showcasing diverse facial attributes. During the training stage, a specific face of a person is used as input, and the FaceID customization model is forced to generate another image of the same person but with altered facial features. Experiments show that models fine-tuned on the CrossFaceID dataset its performance in preserving FaceID fidelity while significantly improving its
  arXiv  Detail & Related papers  (2025-01-26T05:27:38Z)
• OSDFace: One-Step Diffusion Model for Face Restoration [72.5045389847792]
  Diffusion models have demonstrated impressive performance in face restoration. We propose OSDFace, a novel one-step diffusion model for face restoration. 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)
• TCDiff: Triple Condition Diffusion Model with 3D Constraints for Stylizing Synthetic Faces [1.7535229154829601]
  Face recognition experiments using 1k, 2k, and 5k classes of our new dataset for training outperform state-of-the-art synthetic datasets in real face benchmarks.
  arXiv  Detail & Related papers  (2024-09-05T14:59:41Z)
• G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors [71.69161292330504]
  Reversible face anonymization seeks to replace sensitive identity information in facial images with synthesized alternatives. This paper introduces Gtextsuperscript2Face, which leverages both generative and geometric priors to enhance identity manipulation. Our method outperforms existing state-of-the-art techniques in face anonymization and recovery, while preserving high data utility.
  arXiv  Detail & Related papers  (2024-08-18T12:36:47Z)
• Arc2Face: A Foundation Model for ID-Consistent Human Faces [95.00331107591859]
  Arc2Face is an identity-conditioned face foundation model. It can generate diverse photo-realistic images with an unparalleled degree of face similarity than existing models.
  arXiv  Detail & Related papers  (2024-03-18T10:32:51Z)
• MFIM: Megapixel Facial Identity Manipulation [0.6091702876917281]
  We propose a novel face-swapping framework called Megapixel Facial Identity Manipulation (MFIM) Our model exploits pretrained StyleGAN in the manner of GAN-inversion to effectively generate a megapixel image. We show that our model achieves state-of-the-art performance through extensive experiments.
  arXiv  Detail & Related papers  (2023-08-03T04:36:48Z)
• DCFace: Synthetic Face Generation with Dual Condition Diffusion Model [18.662943303044315]
  We propose a Dual Condition Face Generator (DCFace) based on a diffusion model. Our novel Patch-wise style extractor and Time-step dependent ID loss enables DCFace to consistently produce face images of the same subject under different styles with precise control.
  arXiv  Detail & Related papers  (2023-04-14T11:31:49Z)
• Generating 2D and 3D Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution [68.8204255655161]
  A master face is a face image that passes face-based identity authentication for a high percentage of the population. We optimize these faces for 2D and 3D face verification models. In 3D, we generate faces using the 2D StyleGAN2 generator and predict a 3D structure using a deep 3D face reconstruction network.
  arXiv  Detail & Related papers  (2022-11-25T09:15:38Z)
• DotFAN: A Domain-transferred Face Augmentation Network for Pose and Illumination Invariant Face Recognition [94.96686189033869]
  We propose a 3D model-assisted domain-transferred face augmentation network (DotFAN) DotFAN can generate a series of variants of an input face based on the knowledge distilled from existing rich face datasets collected from other domains. Experiments show that DotFAN is beneficial for augmenting small face datasets to improve their within-class diversity.
  arXiv  Detail & Related papers  (2020-02-23T08:16:34Z)

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.