View-Independent Facial Action Unit Detection

Tang, Chuangao; Zheng, Wenming; Yan, Jingwei; Li, Qiang; Li, Yang; Zhang, Tong; Cui, Zhen

doi:10.1109/fg.2017.113

Cited by 31 publications

(25 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2. As can be observed, our model sets a new benchmark in the problem of multi-view action unit detection by improving with an 14% in F1-score over the baseline approach in the Test set, and it is onpar with the challenge winner [44], under that metric. Furthermore, on the ACC metric, we outperform both the baseline and the challenge winner with an absolute improvement of 15% and 4% respectively.…”

Section: Evaluation On Test Setmentioning

confidence: 79%

See 1 more Smart Citation

Multi-view dynamic facial action unit detection

Romero

León

Arbeláez

2022

Image and Vision Computing

View full text Add to dashboard Cite

We propose a novel convolutional neural network approach to address the fine-grained recognition problem of multi-view dynamic facial action unit detection. We leverage recent gains in large-scale object recognition 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. We then explore the design space of our approach by considering both shared and independent representations for separate action units, and also different CNN architectures for combining color and motion information. We then move to the novel setup of the FERA 2017 Challenge, in which we propose a multi-view extension of our approach that operates by first predicting the viewpoint from which the video was taken, and then evaluating an ensemble of action unit detectors that were trained for that specific viewpoint. Our approach is holistic, efficient, and modular, since new action units can be easily included in the overall system. Our approach significantly outperforms the baseline of the FERA 2017 Challenge, with an absolute improvement of 14% on the F1-metric. Additionally, it compares favorably against the winner of the FERA 2017 challenge. Code source is available at https: // github. com/ BCV-Uniandes/ AUNets .

show abstract

Section: Evaluation On Test Setmentioning

confidence: 79%

“…Table 7: Results over FERA17 Validation and Test, using F1-score and Accuracy (ACC). Comparison with the baseline approach [46] and the challenge winner [44].…”

Section: Multi-view Systemmentioning

confidence: 99%

Multi-view dynamic facial action unit detection

Romero

León

Arbeláez

2022

Image and Vision Computing

View full text Add to dashboard Cite

show abstract

“…Occurrence detection Intensity estimation Amirian et al [8] -0.295 Batista et al [18] 0.506 0.399 He et al [73] 0.507 -Li et al [95] 0.495 -Tang et al [166] 0.574 -Zhou et al [218] -0.445 Baseline [181] 0.452 0.217…”

Section: Teammentioning

confidence: 99%

Automatic Analysis of Facial Actions: A Survey

Martínez

Valstar

Jiang

et al. 2019

IEEE Trans. Affective Comput.

238

153

View full text Add to dashboard Cite

Abstract-As one of the most comprehensive and objective ways to describe facial expressions, the Facial Action Coding System (FACS) has recently received significant attention. Over the past 30 years, extensive research has been conducted by psychologists and neuroscientists on various aspects of facial expression analysis using FACS. Automating FACS coding would make this research faster and more widely applicable, opening up new avenues to understanding how we communicate through facial expressions. Such an automated process can also potentially increase the reliability, precision and temporal resolution of coding. This paper provides a comprehensive survey of research into machine analysis of facial actions. We systematically review all components of such systems: pre-processing, feature extraction and machine coding of facial actions. In addition, the existing FACS-coded facial expression databases are summarised. Finally, challenges that have to be addressed to make automatic facial action analysis applicable in real-life situations are extensively discussed. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the future of machine recognition of facial actions: what are the challenges and opportunities that researchers in the field face.

show abstract

“…Acc. 2AFC RMSE ICC PCC [Zhou, et al 2017] MTDN 1080 V 7 6 9 ---0.879 0.446 - [Batista, et al 2017] AUMPNet 1080 V 7 6 9 0.506 ---0.399 - [Li, et al 2017a] multi-AU fusion 1080 V 7 6 9 0.498 0.694 ---- [Tang, et al 2017] VGG 1080 V 7 6 9 0.574 0.778 ---- [Amirian, et al 2017] sparse coding 1080 V 7 6 9 ---0.970 0.295 - [Valstar, et al 2017] CRF or CORF 1080 V 7 6 9 0.452 0.561 0.537 1.403 0.217 0.221…”

Section: F1mentioning

confidence: 99%

Facial Expression Analysis under Partial Occlusion

et al. 2018

View full text Add to dashboard Cite

Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment and human computer interaction. The vast majority of completed FEA studies are based on non-occluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better informed and benchmarked future work.

show abstract

View-Independent Facial Action Unit Detection

Cited by 31 publications

References 23 publications

Multi-view dynamic facial action unit detection

Multi-view dynamic facial action unit detection

Automatic Analysis of Facial Actions: A Survey

Facial Expression Analysis under Partial Occlusion

Contact Info

Product

Resources

About