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computer-vision

Facial Action Unit Detection

3260 papers • 126 benchmarks • 313 datasets

Facial action unit detection is the task of detecting action units from a video of a face - for example, lip tightening and cheek raising. ( Image credit: Self-supervised Representation Learning from Videos for Facial Action Unit Detection )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in facial-action-unit-detection

Trend

Dataset

Best Model

Actions

BP4D

BP4D

DISFA

DISFA

Libraries

i

Use these libraries to find facial-action-unit-detection models and implementations

tomas-gajarsky/facetorch

2 papers 393

Datasets

DISFA

BP4D

CANDOR Corpus

Thermal Face Database

Thermal Face Database

Subtasks

No subtasks available.

Most implemented papers

Multitask Emotion Recognition with Incomplete Labels

Bertram E. Shi, Didan Deng, Zhaokang Chen•Sun Feb 09 2020

This work trains a unified model to perform three tasks: facial action unit detection, expression classification, and valence-arousal estimation, and proposes an algorithm for the multitask model to learn from missing (incomplete) labels.

99

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Facial Action Unit Detection | State-of-the-Art

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Pre-training strategies and datasets for facial representation learning

Georgios Tzimiropoulos, Shiyang Cheng, Adrian Bulat, Jing Yang, A. Garbett, Enrique Sanchez•Mon Mar 29 2021

A comprehensive evaluation benchmark for facial representation learning consisting of 5 important face analysis tasks and systematically investigates two ways of large-scale representation learning applied to faces: supervised and unsupervised pre-training.

41 0

Linear Disentangled Representation Learning for Facial Actions

Xiang Xiang, T. Tran•Tue Jan 10 2017

This paper proposes an elegant linear model to untangle facial actions from expressive face videos which contain a mixture of linearly-representable attributes, and exploits the low-rank property across frames to implicitly subtract the intrinsic neutral face.

21 0

GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

Jiaqi Tang, Xuesong Niu, Hao Lu, Jiyao Wang, Yin Wang, Qingyong Hu, Yuting Zhang, Kaishen Yuan, Bin Huang, Zitong Yu, Dengbo He, Shuiguang Deng, Hao Chen, Yingcong Chen, Shiguang Shan•Fri Mar 08 2024

The results show that GPT-4V has high accuracy in facial action unit recognition and micro-expression detection while its general facial expression recognition performance is not accurate, and provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing.

28 0

Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition

H. Gunes, Siyang Song, Linlin Shen, Cheng Luo, Weicheng Xie•Sun May 01 2022

This paper proposes an AU relationship modelling approach that deep learns a unique graph to explicitly describe the relationship between each pair of AUs of the target facial display and demonstrates large performance improvements for CNN and transformer-based backbones.

163 0

Multi-View Dynamic Facial Action Unit Detection

Pablo Arbeláez, Andrés Romero, Juan León•Mon Apr 24 2017

This work proposes a novel convolutional neural network approach to address the fine-grained recognition problem of multi-view dynamic facial action unit detection by formulating the task of predicting the presence or absence of a specific action unit in a still image of a human face as holistic classification.

22 0

Multi-Scale Promoted Self-Adjusting Correlation Learning for Facial Action Unit Detection

Huanjing Yue, Zitong Yu, Xuesong Niu, Xin Liu, Kaishen Yuan, Jingang Shi, Jingyu Yang•Mon Aug 14 2023

A novel self-adjusting AU-correlation learning (SACL) method with less computation for AU detection that outperforms the state-of-the-art methods on widely used AU detection benchmark datasets and obtains a more robust feature representation for the final AU detection.

20 0

Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment

Jianfei Cai, Lizhuang Ma, Zhiwen Shao, Zhilei Liu•Wed Mar 14 2018

A novel end-to-end deep learning framework for joint AU detection and face alignment, which has not been explored before, in which multi-scale shared features are learned firstly, and high-level features of face alignment are fed into AU detection.

191 0

Unconstrained Facial Action Unit Detection via Latent Feature Domain

Jianfei Cai, Lizhuang Ma, Xuequan Lu, Zhiwen Shao, Tat-Jen Cham•Sun Mar 24 2019

This paper proposes an end-to-end unconstrained facial AU detection framework based on domain adaptation, which transfers accurate AU labels from a constrained source domain to an unconStrained target domain by exploiting labels of AU-related facial landmarks.

11 0

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