FusiformNet: Extracting Discriminative Facial Features on Different
Levels
- URL: http://arxiv.org/abs/2011.00577v3
- Date: Thu, 26 Nov 2020 15:49:48 GMT
- Title: FusiformNet: Extracting Discriminative Facial Features on Different
Levels
- Authors: Kyo Takano
- Abstract summary: I propose FusiformNet, a novel framework for feature extraction that leverages the nature of discriminative facial features.
FusiformNet achieved a state-of-the-art accuracy of 96.67% without labeled outside data, image augmentation, normalization, or special loss functions.
Considering its ability to extract both general and local facial features, the utility of FusiformNet may not be limited to facial recognition but also extend to other DNN-based tasks.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Over the last several years, research on facial recognition based on Deep
Neural Network has evolved with approaches like task-specific loss functions,
image normalization and augmentation, network architectures, etc. However,
there have been few approaches with attention to how human faces differ from
person to person. Premising that inter-personal differences are found both
generally and locally on the human face, I propose FusiformNet, a novel
framework for feature extraction that leverages the nature of discriminative
facial features. Tested on Image-Unrestricted setting of Labeled Faces in the
Wild benchmark, this method achieved a state-of-the-art accuracy of 96.67%
without labeled outside data, image augmentation, normalization, or special
loss functions. Likewise, the method also performed on a par with previous
state-of-the-arts when pre-trained on CASIA-WebFace dataset. Considering its
ability to extract both general and local facial features, the utility of
FusiformNet may not be limited to facial recognition but also extend to other
DNN-based tasks.
Related papers
- Face Anonymization Made Simple [44.24233169815565]
Current face anonymization techniques often depend on identity loss calculated by face recognition models, which can be inaccurate and unreliable.
In contrast, our approach uses diffusion models with only a reconstruction loss, eliminating the need for facial landmarks or masks.
Our model achieves state-of-the-art performance in three key areas: identity anonymization, facial preservation, and image quality.
arXiv Detail & Related papers (2024-11-01T17:45:21Z) - Self-Supervised Facial Representation Learning with Facial Region
Awareness [13.06996608324306]
Self-supervised pre-training has been proven to be effective in learning transferable representations that benefit various visual tasks.
Recent efforts toward this goal are limited to treating each face image as a whole.
We propose a novel self-supervised facial representation learning framework to learn consistent global and local facial representations.
arXiv Detail & Related papers (2024-03-04T15:48:56Z) - CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial
Expression Recognition [80.07590100872548]
We propose Contrastive Inhibitory Adaptati On (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets.
CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations.
arXiv Detail & Related papers (2022-08-10T15:46:05Z) - Enhancing Quality of Pose-varied Face Restoration with Local Weak
Feature Sensing and GAN Prior [29.17397958948725]
We propose a well-designed blind face restoration network with generative facial prior.
Our model performs superior to the prior art for face restoration and face super-resolution tasks.
arXiv Detail & Related papers (2022-05-28T09:23:48Z) - 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) - SynFace: Face Recognition with Synthetic Data [83.15838126703719]
We devise the SynFace with identity mixup (IM) and domain mixup (DM) to mitigate the performance gap.
We also perform a systematically empirical analysis on synthetic face images to provide some insights on how to effectively utilize synthetic data for face recognition.
arXiv Detail & Related papers (2021-08-18T03:41:54Z) - Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo
Collection [65.92058628082322]
Non-parametric face modeling aims to reconstruct 3D face only from images without shape assumptions.
This paper presents a novel Learning to Aggregate and Personalize framework for unsupervised robust 3D face modeling.
arXiv Detail & Related papers (2021-06-15T03:10:17Z) - Dual-discriminator GAN: A GAN way of profile face recognition [21.181356044588213]
We propose a method of generating frontal faces with image-to-image profile faces based on Generative Adversarial Network (GAN)
In this paper, we proposed a method of generating frontal faces with image-to-image profile faces based on Generative Adversarial Network (GAN)
arXiv Detail & Related papers (2020-03-20T06:01:58Z) - 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)
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.