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Found 51 papers, 14 papers with code
Generate to Adapt: Resolution Adaption Network for Surveillance Face Recognition
Although deep learning techniques have largely improved face recognition, unconstrained surveillance face recognition (FR) is still an unsolved challenge, due to the limited training data and the gap of domain distribution.
Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition
We find that FER models remember noisy samples by focusing on a part of the features that can be considered related to the noisy labels instead of learning from the whole features that lead to the latent truth.
Ranked #1 on
Facial Expression Recognition
on FERPlus
(using extra training data)
Exploring Disentangled Content Information for Face Forgery Detection
Convolutional neural network based face forgery detection methods have achieved remarkable results during training, but struggled to maintain comparable performance during testing.
DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose Estimation
To solve this problem, we propose a pose augmentation solution via DH forward kinematics model, which we call DH-AUG. We observe that the previous work is all based on single-frame pose augmentation, if it is directly applied to video pose estimator, there will be several previously ignored problems: (i) angle ambiguity in bone rotation (multiple solutions); (ii) the generated skeleton video lacks movement continuity.
Identifying Rhythmic Patterns for Face Forgery Detection and Categorization
Motivated by this key observation, we propose a framework for face forgery detection and categorization consisting of: 1) a Spatial-Temporal Filtering Network (STFNet) for PPG signals filtering, and 2) a Spatial-Temporal Interaction Network (STINet) for constraint and interaction of PPG signals.
Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher
On the one hand, PT introduces semi-supervised learning method to relieve the shortage of data in FER.
Deep face recognition with clustering based domain adaptation
Despite great progress in face recognition tasks achieved by deep convolution neural networks (CNNs), these models often face challenges in real world tasks where training images gathered from Internet are different from test images because of different lighting condition, pose and image quality.
Cycle Label-Consistent Networks for Unsupervised Domain Adaptation
The cycle label-consistent loss reinforces the consistency between ground-truth labels and pseudo-labels of source samples leading to statistically similar latent representations between source and target domains.
OPOM: Customized Invisible Cloak towards Face Privacy Protection
In this paper, we investigate the face privacy protection from a technology standpoint based on a new type of customized cloak, which can be applied to all the images of a regular user, to prevent malicious face recognition systems from uncovering their identity.
SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition
Deep face recognition has achieved great success due to large-scale training databases and rapidly developing loss functions.
Ranked #1 on
Face Verification
on LFW
(Accuracy metric)
Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms
We introduce the Oracle-MNIST dataset, comprising of 28$\times $28 grayscale images of 30, 222 ancient characters from 10 categories, for benchmarking pattern classification, with particular challenges on image noise and distortion.
Unsupervised Structure-Texture Separation Network for Oracle Character Recognition
Second, transformation is achieved via swapping the learned textures across domains and a classifier for final classification is trained to predict the labels of the transformed scanned characters.
Meta Balanced Network for Fair Face Recognition
Finally, to mitigate the algorithmic bias, we propose a novel meta-learning algorithm, called Meta Balanced Network (MBN), which learns adaptive margins in large margin loss such that the model optimized by this loss can perform fairly across people with different skin tones.
Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing
A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and reassemble different content and style features for a stylized feature space.
Video Question Answering: Datasets, Algorithms and Challenges
Video Question Answering (VideoQA) aims to answer natural language questions according to the given videos.
Augmented Geometric Distillation for Data-Free Incremental Person ReID
On this basis, we reveal a "noisy distillation" problem stemming from the noise in dreaming memory, and further propose to augment distillation in a pairwise and cross-wise pattern over different views of memory to mitigate it.
Federated Learning for Face Recognition with Gradient Correction
With increasing appealing to privacy issues in face recognition, federated learning has emerged as one of the most prevalent approaches to study the unconstrained face recognition problem with private decentralized data.
Relative Uncertainty Learning for Facial Expression Recognition
To quantify these uncertainties and achieve good performance under noisy data, we regard uncertainty as a relative concept and propose an innovative uncertainty learning method called Relative Uncertainty Learning (RUL).
Ranked #2 on
Facial Expression Recognition
on RAF-DB
MLFW: A Database for Face Recognition on Masked Faces
As more and more people begin to wear masks due to current COVID-19 pandemic, existing face recognition systems may encounter severe performance degradation when recognizing masked faces.
Ranked #1 on
Face Recognition
on MLFW
Representative Forgery Mining for Fake Face Detection
Although vanilla Convolutional Neural Network (CNN) based detectors can achieve satisfactory performance on fake face detection, we observe that the detectors tend to seek forgeries on a limited region of face, which reveals that the detectors is short of understanding of forgery.
Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition
Face photo-sketch synthesis and recognition has many applications in digital entertainment and law enforcement.
Face Transformer for Recognition
Therefore, we investigate the performance of Transformer models in face recognition.
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition
The training of a deep face recognition system usually faces the interference of label noise in the training data.
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation
Inspired by the effectiveness of pseudo-labels in domain adaptation, we propose a reinforcement learning based selective pseudo-labeling method for semi-supervised domain adaptation.
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition
In this paper, we propose a simple yet effective approach, named Point Adversarial Self Mining (PASM), to improve the recognition accuracy in facial expression recognition.
RAF-AU Database: In-the-Wild Facial Expressions with Subjective Emotion Judgement and Objective AU Annotations
Much of the work on automatic facial expression recognition relies on databases containing a certain number of emotion classes and their exaggerated facial configurations (generally six prototypical facial expressions), based on Ekman's Basic Emotion Theory.
PropagationNet: Propagate Points to Curve to Learn Structure Information
Deep learning technique has dramatically boosted the performance of face alignment algorithms.
Omni-supervised Facial Expression Recognition via Distilled Data
However, the performance of the current state-of-the-art facial expression recognition (FER) approaches is directly related to the labeled data for training.
Towards Transferable Adversarial Attack against Deep Face Recognition
In particular, the existence of transferable adversarial examples can severely hinder the robustness of DCNNs since this type of attacks can be applied in a fully black-box manner without queries on the target system.
Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning
To encourage fairness, we introduce the idea of adaptive margin to learn balanced performance for different races based on large margin losses.
Metric Classification Network in Actual Face Recognition Scene
In this paper, we train a validation classifier to normalize the decision threshold, which means that the result can be obtained directly without replacing the threshold.
Adversarial Learning with Margin-based Triplet Embedding Regularization
The Deep neural networks (DNNs) have achieved great success on a variety of computer vision tasks, however, they are highly vulnerable to adversarial attacks.
Mixed High-Order Attention Network for Person Re-Identification
Then, rethinking person ReID as a zero-shot learning problem, we propose the Mixed High-Order Attention Network (MHN) to further enhance the discrimination and richness of attention knowledge in an explicit manner.
Ranked #4 on
Person Re-Identification
on CUHK03-C
Hybrid-Attention based Decoupled Metric Learning for Zero-Shot Image Retrieval
In zero-shot image retrieval (ZSIR) task, embedding learning becomes more attractive, however, many methods follow the traditional metric learning idea and omit the problems behind zero-shot settings.
A Deeper Look at Facial Expression Dataset Bias
Datasets play an important role in the progress of facial expression recognition algorithms, but they may suffer from obvious biases caused by different cultures and collection conditions.
Signal-to-Noise Ratio: A Robust Distance Metric for Deep Metric Learning
In this paper, different from the approaches on learning the loss structures, we propose a robust SNR distance metric based on Signal-to-Noise Ratio (SNR) for measuring the similarity of image pairs for deep metric learning.
Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering
However, in this paper, we first emphasize that the generalization ability is a core ingredient of this 'good' embedding as well and largely affects the metric performance in zero-shot settings as a matter of fact.
Racial Faces in-the-Wild: Reducing Racial Bias by Information Maximization Adaptation Network
Racial bias is an important issue in biometric, but has not been thoroughly studied in deep face recognition.
Virtual Class Enhanced Discriminative Embedding Learning
Recently, learning discriminative features to improve the recognition performances gradually becomes the primary goal of deep learning, and numerous remarkable works have emerged.
Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision
We assume that problems inside are inadequate use of supervision information and imbalance between semantics and details at all level feature maps in CNN even with Feature Pyramid Networks (FPN).
ALMN: Deep Embedding Learning with Geometrical Virtual Point Generating
Deep embedding learning becomes more attractive for discriminative feature learning, but many methods still require hard-class mining, which is computationally complex and performance-sensitive.
Deep Facial Expression Recognition: A Survey
We then introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets.
Deep Face Recognition: A Survey
Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction.
Deep Visual Domain Adaptation: A Survey
Deep domain adaption has emerged as a new learning technique to address the lack of massive amounts of labeled data.
Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments
Labeled Faces in the Wild (LFW) database has been widely utilized as the benchmark of unconstrained face verification and due to big data driven machine learning methods, the performance on the database approaches nearly 100%.
Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation
In this paper, we first emphasize that the early saturation behavior of softmax will impede the exploration of SGD, which sometimes is a reason for model converging at a bad local-minima, then propose Noisy Softmax to mitigating this early saturation issue by injecting annealed noise in softmax during each iteration.
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild
Past research on facial expressions have used relatively limited datasets, which makes it unclear whether current methods can be employed in real world.
Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models
In this paper, our generative model trained with synthetic images rendered from 3D models reduces the workload of data collection and limitation of conditions.
Multi-Manifold Deep Metric Learning for Image Set Classification
In this paper, we propose a multi-manifold deep metric learning (MMDML) method for image set classification, which aims to recognize an object of interest from a set of image instances captured from varying viewpoints or under varying illuminations.
Linear Ranking Analysis
We extend the classical linear discriminant analysis (LDA) technique to linear ranking analysis (LRA), by considering the ranking order of classes centroids on the projected subspace.
In Defense of Sparsity Based Face Recognition
The success of sparse representation based classification (SRC) has largely boosted the research of sparsity based face recognition in recent years.
