Prior Aided Streaming Network for Multi-task Affective Analysis

Zhang, Wei; Guo, Zunhu; Chen, Keyu; Li, Lincheng; Zhang, Zhimeng; Ding, Yu; Wu, Runze; Tangjie, Lv,; Fan, Changjie

doi:10.1109/iccvw54120.2021.00394

Cited by 22 publications

(4 citation statements)

References 48 publications

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“…This approach highlights the effectiveness of bifurcated networks in handling complex facial recognition tasks in varied conditions. Continuing this trend, Zhang et al [ 76 ] combine datasets Aff-Wild2, BP4D, and DFEW, utilizing a CNN for feature extraction and obtaining a 79.3% accuracy rate. Shabbir et al [ 90 ] propose the Fine-Grained Bilinear CNN (FgbCNN) to enhance the generalization of facial features in expressions such as anger and fear, achieving high accuracies on multiple datasets.…”

Section: Resultsmentioning

confidence: 99%

Systematic Review of Emotion Detection with Computer Vision and Deep Learning

Pereira,

Mendes,

Ribeiro

et al. 2024

Sensors

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Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human–computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this paper, we conduct a systematic review of facial and pose emotion recognition using DL and computer vision, analyzing and evaluating 77 papers from different sources under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our review covers several topics, including the scope and purpose of the studies, the methods employed, and the used datasets. The scope of this work is to conduct a systematic review of facial and pose emotion recognition using DL methods and computer vision. The studies were categorized based on a proposed taxonomy that describes the type of expressions used for emotion detection, the testing environment, the currently relevant DL methods, and the datasets used. The taxonomy of methods in our review includes Convolutional Neural Network (CNN), Faster Region-based Convolutional Neural Network (R-CNN), Vision Transformer (ViT), and “Other NNs”, which are the most commonly used models in the analyzed studies, indicating their trendiness in the field. Hybrid and augmented models are not explicitly categorized within this taxonomy, but they are still important to the field. This review offers an understanding of state-of-the-art computer vision algorithms and datasets for emotion recognition through facial expressions and body poses, allowing researchers to understand its fundamental components and trends.

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Section: Resultsmentioning

confidence: 99%

Systematic Review of Emotion Detection with Computer Vision and Deep Learning

Pereira,

Mendes,

Ribeiro

et al. 2024

Sensors

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“…We verify the effectiveness of our proposed prior embedding module and streaming model respectively. For the prior embedding module, we conduct ablation study and prove that our method not only concentrates on the facial areas mostly and conveys human emotions, but also ignores the noise areas like backgrounds 12 . For the streaming module, we compare our method with/without the AU→EXPR, AU→VA, and EXPR→VA modules.…”

Section: Automatic Affective Recognitionmentioning

confidence: 97%

A new CAPTCHA based on facial expression recognition

2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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Computers and humans differ in their logic and approach when it comes to recognizing emotions, especially when it comes to micro expressions. Using this difference, we make a new CAPTCHA based on a new facial expression recognition algorithm. This new CAPTCHA requires the user to find a similar expression to the given picture and rotate the picture to fit the background. After that, we counter-attacked the CAPTCHA, and the probability of being successfully attacked was less than 1%. We also conducted user tests based on this. CAPTCHA is superior to others in terms of recognition success rate and recognition time and has a good application significance.

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“…For example, in affective analysis, human emotion comes with varying representation forms, including category expression, action units and valence/arousal. They are inherently related, and based on this, several multi-tasking frameworks have been exploited [166], [167]. Besides, in [168], the authors simultaneously predicted the systolic and diastolic blood pressure from PPG, achieving better performance than directly estimating each individual parameter.…”

Section: Learn With Supplementary Tasksmentioning

confidence: 99%

Beyond Supervised Learning for Pervasive Healthcare

Deligianni

Han

et al. 2024

IEEE Rev. Biomed. Eng.

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The integration of machine/deep learning and sensing technologies is transforming healthcare and medical practice. However, inherent limitations in healthcare data, namely scarcity, quality, and heterogeneity, hinder the effectiveness of supervised learning techniques which are mainly based on pure statistical fitting between data and labels. In this paper, we first identify the challenges present in machine learning for pervasive healthcare and we then review the current trends beyond fully supervised learning that are developed to address these three issues. Rooted in the inherent drawbacks of empirical risk minimization that underpins pure fully supervised learning, this survey summarizes seven key lines of learning strategies, to promote the generalization performance for real-world deployment. In addition, we point out several directions that are emerging and promising in this area, to develop data-efficient, scalable, and trustworthy computational models, and to leverage multi-modality and multi-source sensing informatics, for pervasive healthcare.

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Prior Aided Streaming Network for Multi-task Affective Analysis

Cited by 22 publications

References 48 publications

Systematic Review of Emotion Detection with Computer Vision and Deep Learning

Systematic Review of Emotion Detection with Computer Vision and Deep Learning

A new CAPTCHA based on facial expression recognition

Beyond Supervised Learning for Pervasive Healthcare

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