Localization using Multi-Focal Spatial Attention for Masked Face
Recognition
- URL: http://arxiv.org/abs/2305.01905v2
- Date: Thu, 7 Sep 2023 08:13:58 GMT
- Title: Localization using Multi-Focal Spatial Attention for Masked Face
Recognition
- Authors: Yooshin Cho, Hanbyel Cho, Hyeong Gwon Hong, Jaesung Ahn, Dongmin Cho,
JungWoo Chang, and Junmo Kim
- Abstract summary: It is necessary to develop masked Face Recognition for contactless biometric recognition systems.
We propose Complementary Attention Learning and Multi-Focal Spatial Attention that precisely removes masked region.
We evaluate the MFR performance on the ICCV 2021-MFR/Insightface track, and demonstrate the improved performance on the both MFR and FR datasets.
- Score: 22.833899749506394
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Since the beginning of world-wide COVID-19 pandemic, facial masks have been
recommended to limit the spread of the disease. However, these masks hide
certain facial attributes. Hence, it has become difficult for existing face
recognition systems to perform identity verification on masked faces. In this
context, it is necessary to develop masked Face Recognition (MFR) for
contactless biometric recognition systems. Thus, in this paper, we propose
Complementary Attention Learning and Multi-Focal Spatial Attention that
precisely removes masked region by training complementary spatial attention to
focus on two distinct regions: masked regions and backgrounds. In our method,
standard spatial attention and networks focus on unmasked regions, and extract
mask-invariant features while minimizing the loss of the conventional Face
Recognition (FR) performance. For conventional FR, we evaluate the performance
on the IJB-C, Age-DB, CALFW, and CPLFW datasets. We evaluate the MFR
performance on the ICCV2021-MFR/Insightface track, and demonstrate the improved
performance on the both MFR and FR datasets. Additionally, we empirically
verify that spatial attention of proposed method is more precisely activated in
unmasked regions.
Related papers
- A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking [0.5898893619901381]
Masked face recognition (MFR) has emerged as a critical domain in biometric identification, especially by the global COVID-19 pandemic.
This survey paper presents a comprehensive analysis of the challenges and advancements in recognising and detecting individuals with masked faces.
arXiv Detail & Related papers (2024-05-09T16:52:43Z) - Seeing through the Mask: Multi-task Generative Mask Decoupling Face
Recognition [47.248075664420874]
Current general face recognition system suffers from serious performance degradation when encountering occluded scenes.
This paper proposes a Multi-task gEnerative mask dEcoupling face Recognition (MEER) network to jointly handle these two tasks.
We first present a novel mask decoupling module to disentangle mask and identity information, which makes the network obtain purer identity features from visible facial components.
arXiv Detail & Related papers (2023-11-20T03:23:03Z) - Attribute-Guided Encryption with Facial Texture Masking [64.77548539959501]
We propose Attribute Guided Encryption with Facial Texture Masking to protect users from unauthorized facial recognition systems.
Our proposed method produces more natural-looking encrypted images than state-of-the-art methods.
arXiv Detail & Related papers (2023-05-22T23:50:43Z) - A Unified Framework for Masked and Mask-Free Face Recognition via
Feature Rectification [19.417191498842044]
We propose a unified framework, named Face Feature Rectification Network (FFR-Net), for recognizing both masked and mask-free faces alike.
We introduce rectification blocks to rectify features extracted by a state-of-the-art recognition model, in both spatial and channel dimensions.
Experiments show that our framework can learn a rectified feature space for recognizing both masked and mask-free faces effectively.
arXiv Detail & Related papers (2022-02-15T12:37:59Z) - Adversarial Mask: Real-World Adversarial Attack Against Face Recognition
Models [66.07662074148142]
We propose a physical adversarial universal perturbation (UAP) against state-of-the-art deep learning-based facial recognition models.
In our experiments, we examined the transferability of our adversarial mask to a wide range of deep learning models and datasets.
We validated our adversarial mask effectiveness in real-world experiments by printing the adversarial pattern on a fabric medical face mask.
arXiv Detail & Related papers (2021-11-21T08:13:21Z) - 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) - My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face
Recognition [4.171626860914305]
We address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS.
We propose a methodology that combines the traditional triplet loss and the mean squared error (MSE) intending to improve the robustness of an MFR system in the masked-unmasked comparison mode.
arXiv Detail & Related papers (2021-08-02T15:51:15Z) - 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) - Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face
Presentation Attack Detection [103.7264459186552]
Face presentation attack detection (PAD) is essential to secure face recognition systems.
Most existing 3D mask PAD benchmarks suffer from several drawbacks.
We introduce a largescale High-Fidelity Mask dataset to bridge the gap to real-world applications.
arXiv Detail & Related papers (2021-04-13T12:48:38Z) - Unmasking Face Embeddings by Self-restrained Triplet Loss for Accurate
Masked Face Recognition [6.865656740940772]
We present a solution to improve the masked face recognition performance.
Specifically, we propose the Embedding Unmasking Model (EUM) operated on top of existing face recognition models.
We also propose a novel loss function, the Self-restrained Triplet (SRT), which enabled the EUM to produce embeddings similar to these of unmasked faces of the same identities.
arXiv Detail & Related papers (2021-03-02T13:43:11Z)
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.