4.6 Article

Real emotion seeker: recalibrating annotation for facial expression recognition

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Engineering, Electrical & Electronic

Joint Expression Synthesis and Representation Learning for Facial Expression Recognition

Xi Zhang et al.

Summary: This paper proposes an end-to-end deep model for simultaneous facial expression recognition and facial image synthesis. The model is based on Generative Adversarial Network (GAN) and has several merits: the performance of facial image synthesis and facial expression recognition tasks can be boosted through the unified model, paired images are not required in the facial image synthesis network, the generated facial images can expand the training set and ease overfitting, different expressions are encoded in a disentangled manner, allowing for synthesizing facial images with arbitrary expressions.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2022)

Article Computer Science, Artificial Intelligence

Revisiting Facial Age Estimation With New Insights From Instance Space Analysis

Kate Smith-Miles et al.

Summary: This paper highlights the potential bias of drawing conclusions based on average performance and introduces the opportunities offered by instance space analysis. By revisiting a comparative study of facial age estimation algorithms, the case study demonstrates the visual insights and research value provided by instance space analysis. The study reveals the hidden bias in well-studied datasets and its impact on algorithm performance conclusions.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

Article Computer Science, Information Systems

Practical age estimation using deep label distribution learning

Huiying Zhang et al.

Summary: This study proposes a more practical approach for facial age recognition, which limits the age label distribution to only cover a reasonable number of neighboring ages, and explores different label distributions to improve model performance. The experimental results show that the proposed method is more effective for facial age recognition compared to the current state-of-the-art framework DLDL.

FRONTIERS OF COMPUTER SCIENCE (2021)

Article Computer Science, Artificial Intelligence

Label Enhancement for Label Distribution Learning

Ning Xu et al.

Summary: Label distribution learning covers a certain number of labels and the process of recovering label distributions can enhance the supervision information in training sets, leading to better learning performance.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2021)

Proceedings Paper Computer Science, Artificial Intelligence

TransFER: Learning Relation-aware Facial Expression Representations with Transformers

Fanglei Xue et al.

Summary: The TransFER model proposed in this study utilizes MAD, ViT-FER, and MSAD components to learn rich relation-aware local representations. By exploring diverse local patches and building rich relations through ViT, the model enhances FER performance effectively.

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition

Delian Ruan et al.

Summary: This paper proposes a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition. Experimental results demonstrate that the proposed method consistently achieves higher recognition accuracy than several state-of-the-art methods on various databases.

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition

Jiahui She et al.

Summary: This paper introduces a solution named DMUE, which addresses the annotation ambiguity issue in Facial Expression Recognition from two perspectives: latent distribution mining and pairwise uncertainty estimation. The method achieves leading performance on popular real-world benchmarks and synthetic noisy datasets.

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Facial Expression Recognition in the Wild via Deep Attentive Center Loss

Amir Hossein Farzaneh et al.

Summary: The proposed Deep Attentive Center Loss (DACL) method adaptsively selects a subset of significant feature elements to enhance discrimination. By integrating an attention mechanism into CNN's intermediate spatial feature maps, DACL estimates attention weights correlated with feature importance.

2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WACV 2021 (2021)

Article Computer Science, Artificial Intelligence

Learning Deep Global Multi-Scale and Local Attention Features for Facial Expression Recognition in the Wild

Zengqun Zhao et al.

Summary: This paper proposed a global multi-scale and local attention network for facial expression recognition in the wild, achieving state-of-the-art results on several benchmarks.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Suppressing Uncertainties for Large-Scale Facial Expression Recognition

Kai Wang et al.

2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2020)

Article Computer Science, Artificial Intelligence

Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition

Kai Wang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

Article Computer Science, Information Systems

Pyramid With Super Resolution for In-the-Wild Facial Expression Recognition

Thanh-Hung Vo et al.

IEEE ACCESS (2020)

Article Computer Science, Artificial Intelligence

A Unified Deep Model for Joint Facial Expression Recognition, Face Synthesis, and Face Alignment

Feifei Zhang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

Article Computer Science, Artificial Intelligence

Geometry Guided Pose-Invariant Facial Expression Recognition

Feifei Zhang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

Article Computer Science, Artificial Intelligence

AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild

Ali Mollahosseini et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Occlusion Robust Face Recognition Based on Mask Learning With Pairwise Differential Siamese Network

Lingxue Song et al.

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild

Shan Li et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Article Computer Science, Artificial Intelligence

Label Distribution Learning

Xin Geng

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2016)

Proceedings Paper Computer Science, Artificial Intelligence

Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution

Emad Barsoum et al.

ICMI'16: PROCEEDINGS OF THE 18TH ACM INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (2016)

Article Computer Science, Artificial Intelligence

ImageNet Large Scale Visual Recognition Challenge

Olga Russakovsky et al.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2015)

Article Computer Science, Artificial Intelligence

Facial expression recognition from near-infrared videos

Guoying Zhao et al.

IMAGE AND VISION COMPUTING (2011)