Federated Learning for Face Recognition via Intra-subject Self-supervised Learning

• URL: http://arxiv.org/abs/2407.16289v1
• Date: Tue, 23 Jul 2024 08:43:42 GMT
• Title: Federated Learning for Face Recognition via Intra-subject Self-supervised Learning
• Authors: Hansol Kim, Hoyeol Choi, Youngjun Kwak,
• Abstract summary: We propose FedFS (Federated Learning for personalized Face recognition via intra-subject Self-supervised learning framework) to train personalized face recognition models without imposing subjects. FedFS comprises two crucial components that leverage aggregated features of the local and global models to cooperate with representations of an off-the-shelf model. We conduct comprehensive experiments on the DigiFace-1M and VGGFace datasets, demonstrating superior performance compared to previous methods.
• Score: 3.9899461012388504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) for face recognition aggregates locally optimized models from individual clients to construct a generalized face recognition model. However, previous studies present two major challenges: insufficient incorporation of self-supervised learning and the necessity for clients to accommodate multiple subjects. To tackle these limitations, we propose FedFS (Federated Learning for personalized Face recognition via intra-subject Self-supervised learning framework), a novel federated learning architecture tailored to train personalized face recognition models without imposing subjects. Our proposed FedFS comprises two crucial components that leverage aggregated features of the local and global models to cooperate with representations of an off-the-shelf model. These components are (1) adaptive soft label construction, utilizing dot product operations to reformat labels within intra-instances, and (2) intra-subject self-supervised learning, employing cosine similarity operations to strengthen robust intra-subject representations. Additionally, we introduce a regularization loss to prevent overfitting and ensure the stability of the optimized model. To assess the effectiveness of FedFS, we conduct comprehensive experiments on the DigiFace-1M and VGGFace datasets, demonstrating superior performance compared to previous methods.

Related papers

• CLFace: A Scalable and Resource-Efficient Continual Learning Framework for Lifelong Face Recognition [0.0]
  CLFace is a continual learning framework designed to preserve and incrementally extend the learned knowledge. It eliminates the classification layer, resulting in a resource-efficient FR model that remains fixed throughout lifelong learning. It incorporates a geometry-preserving distillation scheme to maintain the orientation of the teacher model's feature embedding.
  arXiv  Detail & Related papers  (2024-11-21T06:55:43Z)
• Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset [94.13848736705575]
  We introduce Facial Identity Unlearning Benchmark (FIUBench), a novel VLM unlearning benchmark designed to robustly evaluate the effectiveness of unlearning algorithms. We apply a two-stage evaluation pipeline that is designed to precisely control the sources of information and their exposure levels. Through the evaluation of four baseline VLM unlearning algorithms within FIUBench, we find that all methods remain limited in their unlearning performance.
  arXiv  Detail & Related papers  (2024-11-05T23:26:10Z)
• AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learning [14.139432393751298]
  We propose a simple yet effective federated face recognition framework called AdaFedFR. Our work delicately utilizes feature representations of public identities as learnable negative knowledge to optimize the local objective. Experimental results demonstrate that our method outperforms previous approaches on several prevalent face recognition benchmarks within less than 3 communication rounds.
  arXiv  Detail & Related papers  (2024-05-22T09:19:25Z)
• ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning [57.91881829308395]
  Identity-preserving text-to-image generation (ID-T2I) has received significant attention due to its wide range of application scenarios like AI portrait and advertising. We present textbfID-Aligner, a general feedback learning framework to enhance ID-T2I performance.
  arXiv  Detail & Related papers  (2024-04-23T18:41:56Z)
• Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition [1.4374467687356276]
  This paper presents an innovative approach integrating the MAE-Face self-supervised learning (SSL) method and multi-view Fusion Attention mechanism for expression classification. We suggest easy-to-implement and no-training frameworks aimed at highlighting key facial features to determine if such features can serve as guides for the model. The efficacy of this method is validated by improvements in model performance on the Aff-wild2 dataset.
  arXiv  Detail & Related papers  (2024-03-19T16:21:47Z)
• Faceptor: A Generalist Model for Face Perception [52.8066001012464]
  Faceptor is proposed to adopt a well-designed single-encoder dual-decoder architecture. Layer-Attention into Faceptor enables the model to adaptively select features from optimal layers to perform the desired tasks. Our training framework can also be applied to auxiliary supervised learning, significantly improving performance in data-sparse tasks such as age estimation and expression recognition.
  arXiv  Detail & Related papers  (2024-03-14T15:42:31Z)
• Robust Training of Federated Models with Extremely Label Deficiency [84.00832527512148]
  Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. We propose a novel twin-model paradigm, called Twin-sight, designed to enhance mutual guidance by providing insights from different perspectives of labeled and unlabeled data. Our comprehensive experiments on four benchmark datasets provide substantial evidence that Twin-sight can significantly outperform state-of-the-art methods across various experimental settings.
  arXiv  Detail & Related papers  (2024-02-22T10:19:34Z)
• Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher [52.2926020848095]
  Federated learning is vulnerable to white-box attacks and struggles to adapt to heterogeneous clients. This paper proposes a selective knowledge sharing mechanism for FD, termed Selective-FD.
  arXiv  Detail & Related papers  (2023-04-04T12:04:19Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Federated Self-supervised Learning for Heterogeneous Clients [20.33482170846688]
  We propose a unified and systematic framework, emphHeterogeneous Self-supervised Federated Learning (Hetero-SSFL) for enabling self-supervised learning with federation on heterogeneous clients. The proposed framework allows representation learning across all the clients without imposing architectural constraints or requiring presence of labeled data. We empirically demonstrate that our proposed approach outperforms the state of the art methods by a significant margin.
  arXiv  Detail & Related papers  (2022-05-25T05:07:44Z)
• A Closer Look at Personalization in Federated Image Classification [33.27317065917578]
  Federated Learning (FL) is developed to learn a single global model across the decentralized data. This paper shows that it is possible to achieve flexible personalization after the convergence of the global model. We propose RepPer, an independent two-stage personalized FL framework.
  arXiv  Detail & Related papers  (2022-04-22T06:32:18Z)

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.