Papers with Code 아카이브
Facial Expression Recognition (FER)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 25
- 보존된 평가 기록
- 167
Facial Expression Recognition (FER) is a computer vision task aimed at identifying and categorizing emotional expressions depicted on a human face. The goal is to automate the process of determining emotions in real-time, by analyzing the various features of a face such as eyebrows, eyes, mouth, and other features, and mapping them to a set of emotions such as anger, fear, surprise, sadness and happiness.
<span style="color:grey; opacity: 0.6">( Image credit: DeXpression )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution
Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-Processing
FG 2025 TrustFAA: the First Workshop on Towards Trustworthy Facial Affect Analysis: Advancing Insights of Fairness, Explainability, and Safety (TrustFAA)
EfficientFER: EfficientNetv2 Based Deep Learning Approach for Facial Expression Recognition
Temporal and Spatial Feature Fusion Framework for Dynamic Micro Expression Recognition
TKFNet: Learning Texture Key Factor Driven Feature for Facial Expression Recognition
MELLM: Exploring LLM-Powered Micro-Expression Understanding Enhanced by Subtle Motion Perception
Achieving 3D Attention via Triplet Squeeze and Excitation Block
Tracking Articulatory Dynamics in Speech with a Fixed-Weight BiLSTM-CNN Architecture
SDAFE: A Dual-filter Stable Diffusion Data Augmentation Method for Facial Expression Recognition
AMMSM: Adaptive Motion Magnification and Sparse Mamba for Micro-Expression Recognition
Evaluating Facial Expression Recognition Datasets for Deep Learning: A Benchmark Study with Novel Similarity Metrics
Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data
V-NAW: Video-based Noise-aware Adaptive Weighting for Facial Expression Recognition
Biased Heritage: How Datasets Shape Models in Facial Expression Recognition
Rank-O-ToM: Unlocking Emotional Nuance Ranking to Enhance Affective Theory-of-Mind
Faces of Fairness: Examining Bias in Facial Expression Recognition Datasets and Models
Navigating Label Ambiguity for Facial Expression Recognition in the Wild
A Benchmark for Incremental Micro-expression Recognition
Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design
Emotion estimation from video footage with LSTM
EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition
AHMSA-Net: Adaptive Hierarchical Multi-Scale Attention Network for Micro-Expression Recognition
Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression Recognition
Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognition
Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition
Bidirectional Topic Matching: Quantifying Thematic Overlap Between Corpora Through Topic Modelling
Unimodal and Multimodal Static Facial Expression Recognition for Virtual Reality Users with EmoHeVRDB
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition
Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation
A Cross-Corpus Speech Emotion Recognition Method Based on Supervised Contrastive Learning
Prior-based Objective Inference Mining Potential Uncertainty for Facial Expression Recognition
A survey on Graph Deep Representation Learning for Facial Expression Recognition
VocalTweets: Investigating Social Media Offensive Language Among Nigerian Musicians
Smile upon the Face but Sadness in the Eyes: Emotion Recognition based on Facial Expressions and Eye Behaviors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.