Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition

• URL: http://arxiv.org/abs/2209.04920v2
• Date: Fri, 1 Sep 2023 20:51:48 GMT
• Title: Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition
• Authors: Thanh-Dat Truong, Chi Nhan Duong, Ngan Le, Marios Savvides, Khoa Luu
• Abstract summary: We introduce a new method named Attention-based Bijective Generative Adversarial Networks in a Distillation framework (DAB-GAN) The DAB-GAN method includes a novel attention-based generative structure with the newly defined Bijective Metrics Learning approach. We have evaluated our method on the challenging face recognition databases.
• Score: 36.23997331928846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we investigate the problem of face reconstruction given a facial feature representation extracted from a blackbox face recognition engine. Indeed, it is a very challenging problem in practice due to the limitations of abstracted information from the engine. We, therefore, introduce a new method named Attention-based Bijective Generative Adversarial Networks in a Distillation framework (DAB-GAN) to synthesize the faces of a subject given his/her extracted face recognition features. Given any unconstrained unseen facial features of a subject, the DAB-GAN can reconstruct his/her facial images in high definition. The DAB-GAN method includes a novel attention-based generative structure with the newly defined Bijective Metrics Learning approach. The framework starts by introducing a bijective metric so that the distance measurement and metric learning process can be directly adopted in the image domain for an image reconstruction task. The information from the blackbox face recognition engine will be optimally exploited using the global distillation process. Then an attention-based generator is presented for a highly robust generator to synthesize realistic faces with ID preservation. We have evaluated our method on the challenging face recognition databases, i.e., CelebA, LFW, CFP-FP, CP-LFW, AgeDB, CA-LFW, and consistently achieved state-of-the-art results. The advancement of DAB-GAN is also proven in both image realism and ID preservation properties.

Related papers

• 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)
• 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)
• Attribute-preserving Face Dataset Anonymization via Latent Code Optimization [64.4569739006591]
  We present a task-agnostic anonymization procedure that directly optimize the images' latent representation in the latent space of a pre-trained GAN. We demonstrate through a series of experiments that our method is capable of anonymizing the identity of the images whilst -- crucially -- better-preserving the facial attributes.
  arXiv  Detail & Related papers  (2023-03-20T17:34:05Z)
• DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain [23.4606547767188]
  DuetFace is a privacy-preserving face recognition method that employs collaborative inference in the frequency domain. The proposed method achieves a comparable recognition accuracy and cost to the unprotected ArcFace and outperforms the state-of-the-art privacy-preserving methods.
  arXiv  Detail & Related papers  (2022-07-15T08:35:44Z)
• Graph-based Generative Face Anonymisation with Pose Preservation [49.18049578591058]
  AnonyGAN is a GAN-based solution for face anonymisation. It replaces the visual information corresponding to a source identity with a condition identity provided as any single image.
  arXiv  Detail & Related papers  (2021-12-10T12:58:17Z)
• End2End Occluded Face Recognition by Masking Corrupted Features [82.27588990277192]
  State-of-the-art general face recognition models do not generalize well to occluded face images. This paper presents a novel face recognition method that is robust to occlusions based on a single end-to-end deep neural network. Our approach, named FROM (Face Recognition with Occlusion Masks), learns to discover the corrupted features from the deep convolutional neural networks, and clean them by the dynamically learned masks.
  arXiv  Detail & Related papers  (2021-08-21T09:08:41Z)
• Towards NIR-VIS Masked Face Recognition [47.00916333095693]
  Near-infrared to visible (NIR-VIS) face recognition is the most common case in heterogeneous face recognition. We propose a novel training method to maximize the mutual information shared by the face representation of two domains. In addition, a 3D face reconstruction based approach is employed to synthesize masked face from the existing NIR image.
  arXiv  Detail & Related papers  (2021-04-14T10:40:09Z)
• Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition [29.106746021757168]
  This paper presents a novel generative structure with Bijective Metric Learning, namely Bijective Generative Adrial Networks in a Distillation framework (DiBiGAN) Results on several benchmarking datasets have demonstrated the effectiveness of DiBiGAN on both image realism and ID preservation properties.
  arXiv  Detail & Related papers  (2020-03-16T00:30:54Z)
• 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.