Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement

• URL: http://arxiv.org/abs/2206.04854v1
• Date: Fri, 10 Jun 2022 03:01:33 GMT
• Title: Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement
• Authors: Ziming Yang, Jian Liang, Chaoyou Fu, Mandi Luo, Xiao-Yu Zhang
• Abstract summary: Heterogeneous Face Recognition (HFR) aims to match faces across different domains. We propose a new HFR method named Face Synthesis with Identity-Attribute Disentanglement (FSIAD) FSIAD decouples face images into identity-related representations and identity-unrelated representations (called attributes)
• Score: 33.42679052386639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Heterogeneous Face Recognition (HFR) aims to match faces across different domains (e.g., visible to near-infrared images), which has been widely applied in authentication and forensics scenarios. However, HFR is a challenging problem because of the large cross-domain discrepancy, limited heterogeneous data pairs, and large variation of facial attributes. To address these challenges, we propose a new HFR method from the perspective of heterogeneous data augmentation, named Face Synthesis with Identity-Attribute Disentanglement (FSIAD). Firstly, the identity-attribute disentanglement (IAD) decouples face images into identity-related representations and identity-unrelated representations (called attributes), and then decreases the correlation between identities and attributes. Secondly, we devise a face synthesis module (FSM) to generate a large number of images with stochastic combinations of disentangled identities and attributes for enriching the attribute diversity of synthetic images. Both the original images and the synthetic ones are utilized to train the HFR network for tackling the challenges and improving the performance of HFR. Extensive experiments on five HFR databases validate that FSIAD obtains superior performance than previous HFR approaches. Particularly, FSIAD obtains 4.8% improvement over state of the art in terms of VR@FAR=0.01% on LAMP-HQ, the largest HFR database so far.

Related papers

• 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)
• FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping [62.38898610210771]
  We present a new single-stage method for subject face swapping and identity transfer, named FaceDancer. We have two major contributions: Adaptive Feature Fusion Attention (AFFA) and Interpreted Feature Similarity Regularization (IFSR)
  arXiv  Detail & Related papers  (2022-10-19T11:31:38Z)
• When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark [45.31997043789471]
  MTLFace can learn the age-invariant identity-related representation for face recognition while achieving pleasing face synthesis for model interpretation. We release a large cross-age face dataset with age and gender annotations, and a new benchmark specifically designed for tracing long-missing children.
  arXiv  Detail & Related papers  (2022-10-17T07:04:19Z)
• Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination [68.78903256687697]
  In Heterogeneous Face Recognition (HFR), the objective is to match faces across two different domains such as visible and thermal. Recent methods attempting to fill the gap via synthesis have achieved promising results, but their performance is still limited by the scarcity of paired training data. In this paper, we propose a new face hallucination paradigm for HFR, which not only enables data-efficient synthesis but also allows to scale up model training without breaking any privacy policy.
  arXiv  Detail & Related papers  (2022-03-30T20:44:33Z)
• SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis [65.35922024067551]
  We propose a generative adversarial network (GAN) -based model to generate high-quality, identity preserving frontal faces. Specifically, we propose SuperFront-GAN to synthesize a high-resolution (HR), frontal face from one-to-many LR faces with various poses. We integrate a super-resolution side-view module into SF-GAN to preserve identity information and fine details of the side-views in HR space.
  arXiv  Detail & Related papers  (2020-12-07T23:30:28Z)
• DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition [85.94331736287765]
  We formulate HFR as a dual generation problem, and tackle it via a novel Dual Variational Generation (DVG-Face) framework. We integrate abundant identity information of large-scale visible data into the joint distribution. Massive new diverse paired heterogeneous images with the same identity can be generated from noises.
  arXiv  Detail & Related papers  (2020-09-20T09:48:24Z)
• 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.