Local and Global Feature Attention Fusion Network for Face Recognition

• URL: http://arxiv.org/abs/2411.16169v1
• Date: Mon, 25 Nov 2024 07:55:57 GMT
• Title: Local and Global Feature Attention Fusion Network for Face Recognition
• Authors: Wang Yu, Wei Wei,
• Abstract summary: We propose a Local and Global Feature Attention Fusion (LGAF) network based on feature quality. The network adaptively allocates attention between local and global features according to feature quality and obtains more discriminative and high-quality face features. Experimental results demonstrate that the LGAF achieves the best average performance on $4$ validation sets.
• Score: 6.479154287764899
• License:
• Abstract: Recognition of low-quality face images remains a challenge due to invisible or deformation in partial facial regions. For low-quality images dominated by missing partial facial regions, local region similarity contributes more to face recognition (FR). Conversely, in cases dominated by local face deformation, excessive attention to local regions may lead to misjudgments, while global features exhibit better robustness. However, most of the existing FR methods neglect the bias in feature quality of low-quality images introduced by different factors. To address this issue, we propose a Local and Global Feature Attention Fusion (LGAF) network based on feature quality. The network adaptively allocates attention between local and global features according to feature quality and obtains more discriminative and high-quality face features through local and global information complementarity. In addition, to effectively obtain fine-grained information at various scales and increase the separability of facial features in high-dimensional space, we introduce a Multi-Head Multi-Scale Local Feature Extraction (MHMS) module. Experimental results demonstrate that the LGAF achieves the best average performance on $4$ validation sets (CFP-FP, CPLFW, AgeDB, and CALFW), and the performance on TinyFace and SCFace outperforms the state-of-the-art methods (SoTA).

Related papers

• Generalized Face Liveness Detection via De-spoofing Face Generator [58.7043386978171]
  Previous Face Anti-spoofing (FAS) works face the challenge of generalizing in unseen domains. We conduct an Anomalous cue Guided FAS (AG-FAS) method, which leverages real faces for improving model generalization via a De-spoofing Face Generator (DFG) We then propose an Anomalous cue Guided FAS feature extraction Network (AG-Net) to further improve the FAS feature generalization via a cross-attention transformer.
  arXiv  Detail & Related papers  (2024-01-17T06:59:32Z)
• TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment [53.72721476803585]
  Image Quality Assessment (IQA) is a fundamental task in computer vision that has witnessed remarkable progress with deep neural networks. We propose a top-down approach that uses high-level semantics to guide the IQA network to focus on semantically important local distortion regions. A key component of our approach is the proposed cross-scale attention mechanism, which calculates attention maps for lower level features.
  arXiv  Detail & Related papers  (2023-08-06T09:08:37Z)
• Multi-spectral Class Center Network for Face Manipulation Detection and Localization [52.569170436393165]
  We propose a novel Multi-Spectral Class Center Network (MSCCNet) for face manipulation detection and localization. Based on the features of different frequency bands, the MSCC module collects multi-spectral class centers and computes pixel-to-class relations. Applying multi-spectral class-level representations suppresses the semantic information of the visual concepts which is insensitive to manipulated regions of forgery images.
  arXiv  Detail & Related papers  (2023-05-18T08:09:20Z)
• Deep face recognition with clustering based domain adaptation [57.29464116557734]
  We propose a new clustering-based domain adaptation method designed for face recognition task in which the source and target domain do not share any classes. Our method effectively learns the discriminative target feature by aligning the feature domain globally, and, at the meantime, distinguishing the target clusters locally.
  arXiv  Detail & Related papers  (2022-05-27T12:29:11Z)
• MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial Action Units Detection [16.261362598190807]
  The Facial Action Coding System (FACS) encodes the action units (AUs) in facial images. We argue that encoding AU features just from one perspective may not capture the rich contextual information between regional and global face features. We propose a novel Multi-level Graph Reasoning Network (termed MGRR-Net) for facial AU detection.
  arXiv  Detail & Related papers  (2022-04-04T09:47:22Z)
• Adaptively Enhancing Facial Expression Crucial Regions via Local Non-Local Joint Network [37.665344656227624]
  A local non-local joint network is proposed to adaptively light up the facial crucial regions in feature learning of Facial expression recognition. The proposed method achieves more competitive performance compared with several state-of-the art methods on five benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-26T10:58:25Z)
• Region-Aware Face Swapping [23.779312164460695]
  Region-Aware Face Swapping (RAFSwap) network to achieve identity-consistent high-resolution face generation. textbf1) Local Facial Region-Aware (FRA) branch augments local identity-relevant features. textbf2) Global Source Feature-Adaptive (SFA) branch complements global identity-relevant cues. textitFace Mask Predictor (FMP) module incorporated with StyleGAN2 to predict identity-relevant soft facial masks.
  arXiv  Detail & Related papers  (2022-03-09T07:55:45Z)
• Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation [28.721376937882958]
  Gait recognition is one of the most important biometric technologies and has been applied in many fields. Recent gait recognition frameworks represent each gait frame by descriptors extracted from either global appearances or local regions of humans. We propose a novel feature extraction and fusion framework to achieve discriminative feature representations for gait recognition.
  arXiv  Detail & Related papers  (2020-11-03T04:07:13Z)
• 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)
• Dual-Attention GAN for Large-Pose Face Frontalization [59.689836951934694]
  We present a novel Dual-Attention Generative Adversarial Network (DA-GAN) for photo-realistic face frontalization. Specifically, a self-attention-based generator is introduced to integrate local features with their long-range dependencies. A novel face-attention-based discriminator is applied to emphasize local features of face regions.
  arXiv  Detail & Related papers  (2020-02-17T20:00:56Z)
• FakeLocator: Robust Localization of GAN-Based Face Manipulations [19.233930372590226]
  We propose a novel approach, termed FakeLocator, to obtain high localization accuracy, at full resolution, on manipulated facial images. This is the very first attempt to solve the GAN-based fake localization problem with a gray-scale fakeness map. Experimental results on popular FaceForensics++, DFFD datasets and seven different state-of-the-art GAN-based face generation methods have shown the effectiveness of our method.
  arXiv  Detail & Related papers  (2020-01-27T06:15:01Z)

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.