SOTAVerified

Facial Expression Recognition (FER)

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.

( Image credit: DeXpression )

Papers

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Showing 1–10 of 492 papers

TitleStatusHype
Multimodal Prompt Alignment for Facial Expression Recognition—0
EfficientFER: EfficientNetv2 Based Deep Learning Approach for Facial Expression RecognitionCode1
TKFNet: Learning Texture Key Factor Driven Feature for Facial Expression Recognition—0
Achieving 3D Attention via Triplet Squeeze and Excitation Block—0
SDAFE: A Dual-filter Stable Diffusion Data Augmentation Method for Facial Expression Recognition—0
Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target DataCode0
Evaluating Facial Expression Recognition Datasets for Deep Learning: A Benchmark Study with Novel Similarity Metrics—0
V-NAW: Video-based Noise-aware Adaptive Weighting for Facial Expression RecognitionCode0
Biased Heritage: How Datasets Shape Models in Facial Expression RecognitionCode0
Rank-O-ToM: Unlocking Emotional Nuance Ranking to Enhance Affective Theory-of-Mind—0
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All datasetsAffectNetRAF-DBFER2013FER+Acted Facial Expressions In The Wild (AFEW)CK+FERPlusJAFFESFEWAff-Wild2BP4DDISFA

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NorfaceICC0.74—Unverified
2Ours (VGG-F)ICC0.72—Unverified