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Facial Expression Recognition (FER)
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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.
From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos
Subject-Based Domain Adaptation for Facial Expression Recognition
GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition
QAFE-Net: Quality Assessment of Facial Expressions with Landmark Heatmaps
End-to-end transfer learning for speaker-independent cross-language and cross-corpus speech emotion recognition
Towards Generalizable SER: Soft Labeling and Data Augmentation for Modeling Temporal Emotion Shifts in Large-Scale Multilingual Speech
Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition
Benchmarking Deep Facial Expression Recognition: An Extensive Protocol with Balanced Dataset in the Wild
Multi Loss-based Feature Fusion and Top Two Voting Ensemble Decision Strategy for Facial Expression Recognition in the Wild
Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition
FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel Attention
An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition
ASM: Adaptive Sample Mining for In-The-Wild Facial Expression Recognition
Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition
Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation
InFER: A Multi-Ethnic Indian Facial Expression Recognition Dataset
A Generative Framework for Self-Supervised Facial Representation Learning
FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features
Classifying Dementia in the Presence of Depression: A Cross-Corpus Study
MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition
Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition
Catching Elusive Depression via Facial Micro-Expression Recognition
HTNet for micro-expression recognition
Latent-OFER: Detect, Mask, and Reconstruct with Latent Vectors for Occluded Facial Expression Recognition
Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages
LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noise
Active Learning with Contrastive Pre-training for Facial Expression Recognition
HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation
PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition
Cross-corpus Readability Compatibility Assessment for English Texts
Toward Fair Facial Expression Recognition with Improved Distribution Alignment
UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection
Exploring the Boundaries of Semi-Supervised Facial Expression Recognition using In-Distribution, Out-of-Distribution, and Unconstrained Data
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem
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