Facial Expression Recognition (FER)

121 papers with code • 23 benchmarks • 28 datasets

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 )

Libraries

Use these libraries to find Facial Expression Recognition (FER) models and implementations

A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations

NUSTM/FacialMMT Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2023

With the extracted face sequences, we propose a multimodal facial expression-aware emotion recognition model, which leverages the frame-level facial emotion distributions to help improve utterance-level emotion recognition based on multi-task learning.

39
01 Jul 2023

PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition

jlrex/patt-lite 16 Jun 2023

In this paper, a lightweight patch and attention network based on MobileNetV1, referred to as PAtt-Lite, is proposed to improve FER performance under challenging conditions.

21
16 Jun 2023

ReSup: Reliable Label Noise Suppression for Facial Expression Recognition

purpleleaves007/ferdenoise 29 May 2023

To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression.

0
29 May 2023

ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning

takihasan/arbex 2 May 2023

We also employ learnable anchor points in the embedding space with label distributions and multi-head self-attention mechanism to optimize performance against weak predictions with reliability balancing, which is a strategy that leverages anchor points, attention scores, and confidence values to enhance the resilience of label predictions.

3
02 May 2023

Neuromorphic Event-based Facial Expression Recognition

miccunifi/nefer 13 Apr 2023

Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution.

21
13 Apr 2023

Masked Student Dataset of Expressions

sridharsola/msd-e 7 Apr 2023

Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face.

0
07 Apr 2023

A novel facial emotion recognition model using segmentation VGG-19 architecture

VigneshS10/Segmentation-VGG19 International Journal of Information Technology 2023

CNN has shown great potential in FER tasks due to its unique feature extraction strategy compared to regular FER models.

7
24 Mar 2023

Quaternion Orthogonal Transformer for Facial Expression Recognition in the Wild

gabrella/qot 14 Mar 2023

Firstly, to reduce redundancy among features extracted from pre-trained ResNet-50, we use the orthogonal loss to decompose and compact these features into three sets of orthogonal sub-features.

12
14 Mar 2023

SimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the Wild

jymoon0613/simfle 14 Mar 2023

One of the key issues in facial expression recognition in the wild (FER-W) is that curating large-scale labeled facial images is challenging due to the inherent complexity and ambiguity of facial images.

1
14 Mar 2023

POSTER++: A simpler and stronger facial expression recognition network

talented-q/poster_v2 28 Jan 2023

POSTER achieves the state-of-the-art (SOTA) performance in FER by effectively combining facial landmark and image features through two-stream pyramid cross-fusion design.

82
28 Jan 2023