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IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA)

作者： Ichimura, Takumi Kamada, Shin Prefectural Univ Hiroshima Res Org Reg Oriented Studies Adv Artificial Intelligence Project Res Ctr Minami Ku 1-1-71 Ujina Higashi Hiroshima 7348558 Japan Prefectural Univ Hiroshima Fac Management & Informat Syst Minami Ku 1-1-71 Ujina Higashi Hiroshima 7348558 Japan

ISBN: (纸本)9781728124292

affectnet contains more than 1,000,000 facial images which manually annotated for the presence of eight discrete facial expressions and the intensity of valence and arousal. Adaptive structural learning method of DBN (Adaptive DBN) is positioned as a top Deep learning model of classification capability for some large image benchmark databases. The Convolutional Neural Network and Adaptive DBN were trained for affectnet and classification capability was compared. Adaptive DBN showed higher classification ratio. However, the model was not able to classify some test cases correctly because human emotions contain many ambiguous features or patterns leading wrong answer which includes the possibility of being a factor of adversarial examples, due to two or more annotators answer different subjective judgment for an image. In order to distinguish such cases, this paper investigated a re-learning model of Adaptive DBN with two or more child models, where the original trained model can be seen as a parent model and then new child models are generated for some misclassified cases. In addition, an appropriate child model was generated according to difference between two models by using KL divergence. The generated child models showed better performance to classify two emotion categories: 'Disgust' and 'Anger'.

关键词： Adaptive Structural Learning Deep Belief Network Restricted Boltzmann Machine Kullback-Leibler Divergence Child model Facial Expression affectnet

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Label Quality in affectnet: Results of Crowd-Based Re-annotation 1

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6th Asian Conference on Pattern Recognition (ACPR)

作者： Kim, Doo Yon Wallraven, Christian Korea Univ Dept Artificial Intelligence Seoul South Korea Korea Univ Dept Brain & Cognit Engn Seoul South Korea

ISBN: (数字)9783031024443

ISBN: (纸本)9783031024443;9783031024436

affectnet is one of the most popular resources for facial expression recognition (FER) on relatively unconstrained in-the-wild images. Given that images were annotated by only one annotator with limited consistency checks on the data, however, label quality and consistency may be limited. Here, we take a similar approach to a study that re-labeled another, smaller dataset (FER2013) with crowd-based annotations, and report results from a re-labeling and re-annotation of a subset of difficult affectnet faces with 13 people on both expression label, and valence and arousal ratings. Our results show that human labels overall have medium to good consistency, whereas human ratings especially for valence are in excellent agreement. Importantly, however, crowd-based labels are significantly shifting towards neutral and happy categories and crowd-based affective ratings form a consistent pattern different from the original ratings. ResNets fully trained on the original affectnet dataset do not predict human voting patterns, but when weakly-trained do so much better, particularly for valence. Our results have important ramifications for label quality in affective computing.

关键词： Facial expression recognition Crowd annotation affectnet Affective computing

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A Novel Transformer-Based Approach for Adult's Facial Emotion Recognition

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IEEE ACCESS 2025年 13卷 56485-56508页

作者： Nawaz, Uzma Saeed, Zubair Atif, Kamran Natl Univ Sci & Technol Coll Elect & Mech Engn Knowledge & Data Sci Res Ctr Dept Comp & Software Engn Islamabad 44000 Pakistan Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77840 USA Texas A&M Univ Qatar Dept Elect & Comp Engn Doha Qatar Deakin Univ Dept Civil Engn Melbourne Vic 3125 Australia

Adult facial expression recognition (FER) is essential for human-computer interaction, mental health assessment, and social robotics applications because it improves user experiences and emotional well-being. This study presents a novel attention mechanism-based transformer approach designed to capture detailed patterns in facial features and dynamically focus on the most relevant regions for enhanced accuracy. Unlike conventional deep learning approaches, our method integrates an adaptive attention mechanism and dynamic token pruning, which optimizes computational efficiency while maintaining high accuracy. The model is evaluated on five widely used datasets: FER2013, CK+, affectnet, RAF-DB, and AFEW. It achieves state-of-the-art performance, with accuracies of 98.67% on FER2013, 99.52% on CK+, 99.3% on affectnet, 96.3% on AFEW, and 98.45% on RAF-DB. An ablation study further validates the contribution of each model component, and comparisons with CNN-based and transformer-based approaches confirm the effectiveness of the model. These findings establish the proposed method as a significant advancement in FER, which offers a scalable and efficient solution for real-world applications.

关键词： Emotion recognition Transformers Face recognition Accuracy Brain modeling Real-time systems Adaptation models Lighting Human computer interaction Facial features Facial emotion recognition transformers deep learning FER2013 CK plus affectnet AFEW RAF-DB emotion recognition

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头姿鲁棒的双一致性约束半监督表情识别

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中国图象图形学报 2025年第2期30卷 435-450页

作者：王宇键何军张建勋孙仁浩刘学亮重庆理工大学计算机科学与工程学院重庆400054 数据空间研究院合肥230088 合肥工业大学计算机与信息学院合肥230601

目的现有表情识别方法聚焦提升模型的整体识别准确率,对方法的头部姿态鲁棒性研究不充分。在实际应用中,人的头部姿态往往变化多样,影响表情识别效果,因此研究头部姿态对表情识别的影响,并提升模型在该方面的鲁棒性显得尤为重要。为此,... 详细信息

目的现有表情识别方法聚焦提升模型的整体识别准确率,对方法的头部姿态鲁棒性研究不充分。在实际应用中,人的头部姿态往往变化多样,影响表情识别效果,因此研究头部姿态对表情识别的影响,并提升模型在该方面的鲁棒性显得尤为重要。为此,在深入分析头部姿态对表情识别影响的基础上,提出一种能够基于无标签非正脸表情数据提升模型头部姿态鲁棒性的半监督表情识别方法。方法首先按头部姿态对典型表情识别数据集affectnet重新划分,构建了affectnet-Yaw数据集,支持在不同角度上进行模型精度测试,提升了模型对比公平性。其次,提出一种基于双一致性约束的半监督表情识别方法(dual-consistency semi-supervised learning for facial expression recognition,DCSSL),利用空间一致性模块对翻转前后人脸图像的类别激活一致性进行空间约束,使模型训练时更关注面部表情关键区域特征;利用语义一致性模块通过非对称数据增强和自学式学习方法不断地筛选高质量非正脸数据用于模型优化。在无需对非正脸表情数据人工标注的情况下,方法直接从有标签正脸数据和无标签非正脸数据中学习。最后,联合优化了交叉熵损失、空间一致性约束损失和语义一致性约束损失函数,以确保有监督学习和半监督学习之间的平衡。结果实验结果表明,头部姿态对自然场景表情识别有显著影响;提出affectnet-Yaw具有更均衡的头部姿态分布,有效促进了对这种影响的全面评估;DCSSL方法结合空间一致性和语义一致性约束充分利用无标签非正脸表情数据,显著提高了模型在头部姿态变化下的鲁棒性,较MA-NET(multi-scale and local attention network)和EfficientFace全监督方法,平均表情识别精度分别提升了5.40%和17.01%。结论本文提出的双一致性半监督方法能充分利用正脸和非正脸数据,显著提升了模型在头部姿态变化下的表情识别精度;新数据集有效支撑了对头部姿态对表情识别影响的全面评估。

关键词：表情识别(FER) 头部姿态双一致性约束半监督学习 affectnet 图像识别

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Facial expression recognition in the wild, by fusion of deep learnt and hand-crafted features

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COGNITIVE SYSTEMS RESEARCH 2020年 62卷 23-34页

作者： Reddy, G. Viswanatha Savarni, C. V. R. Dharma Mukherjee, Snehasis Indian Inst Informat Technol Sri City Comp Vis Res Grp Sricity Andhra Pradesh India

• Two CNNs are trained with different sampling techniques. • Pre-training and fine tuning are used for training the deep networks. • Combined hand crafted features with CNN features to apply in the wild scenario. • Facial landmark points are used to extract the handcrafted features. • Rigorous experiments on affectnet dataset show superiority of the proposed method. In spite of the recent advancements in the field of deep learning based techniques for facial expression recognition, the efficiency of the state-of-the-art recognition methods in the wild scenarios, remains a challenge. The main reason behind the less efforts made for handling wild scenarios is two-folds: very less and varying levels of cues available to identify the distinguishable patterns of features (spatial and temporal) and non-availability of a big dataset to train a deep learning model. Recently, a huge dataset called affectnet is introduced in the literature providing enough base to apply a deep learning model to train. This paper proposes an efficient combination of hand crafted and deep learning features for facial expression recognition in the wild. We use facial landmark points as hand-crafted features and XceptionNet for the deep learned features. We experiment with XceptionNet and Densenet propose the use of XceptionNet as it performs better compared to DenseNet, when applied on wild scenarios. The proposed fusion of the hand-crafted and XceptionNet features outperforms the state-of-the-art methods for facial expression recognition in the wild. [ABSTRACT FROM AUTHOR]

关键词： affectnet Facial expression Facial landmark points Global average pooling XceptionNet

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SCL-FExR: supervised contrastive learning approach for facial expression Recognition

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第20期82卷 31351-31371页

作者： Vasudeva, Kshitiza Dubey, Akshat Chandran, Saravanan Natl Inst Technol Dept Comp Sci & Engn Durgapur W Bengal India Birla Inst Technol Dept Comp Sci & Engn Ranchi Jharkhand India

Facial Expression Recognition (FER) is a significant field of computer vision and has emerged as a crucial component of Human-computer interaction. Breakthroughs in self-supervised representation learning have resulted from a renaissance of work in contrastive learning, following the state-of-the-art performance in unsupervised training of deep image models. However, due to the random sampling of false negatives for contrastive loss calculation, the representation quality might degrade in FER. In this work, we extend the self-supervised contrastive learning technique to the fully supervised setting to effectively exploit label information in classifying facial expressions. Therefore, we propose a Supervised Contrastive Learning- Facial Expression Recognition (SCL-FExR) system to create a model which is robust for real-world emotion detection. Our goal is not to compete with the highly complex state-of-the-art CNN-based Deep Neural Network, but to establish a method that can be incorporated to achieve similar performance but with less-complex models and more robustness. We demonstrate the effectiveness of the suggested method using three FER datasets: FER2013, affectnet, and CK+. On FER2013, we achieved a similar accuracy of 76%, establishing a method that can be incorporated into less complex CNN-based Deep Neural Networks to achieve robustness and be significantly more noise-resistant. The secondary aim is to show how a data-based strategy may be used to train very complicated deep learning models instead of a model-based approach, which solves the issue of computational expenditure.

关键词： Contrastive training Supervised learning FER2013 affectnet Facial expression Recognition

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Simultaneous prediction of valence / arousal and emotion categories and its application in an HRC scenario

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第1期12卷 57-73页

作者： Handrich, Sebastian Dinges, Laslo Al-Hamadi, Ayoub Werner, Philipp Saxen, Frerk Al Aghbari, Zaher Otto von Guericke Univ Magdeburg Germany Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates

We address the problem of facial expression analysis. The proposed approach predicts both basic emotion and valence/arousal values as a continuous measure for the emotional state. Experimental results including cross-database evaluation on the affectnet, Aff-Wild, and AFEW dataset shows that our approach predicts emotion categories and valence/arousal values with high accuracies and that the simultaneous learning of discrete categories and continuous values improves the prediction of both. In addition, we use our approach to measure the emotional states of users in an Human-Robot-Collaboration scenario (HRC), show how these emotional states are affected by multiple difficulties that arise for the test subjects, and examine how different feedback mechanisms counteract negative emotions users experience while interacting with a robot system.

关键词： Facial expression Valence Arousal HRC affectnet AFEW Aff-Wild

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Discussions of Different Deep Transfer Learning Models for Emotion Recognitions

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IEEE ACCESS 2022年 10卷 102860-102875页

作者： Yen, Chih-Ta Li, Kang-Hua Natl Taiwan Ocean Univ Dept Elect Engn Keelung 202301 Taiwan Natl Formosa Univ Dept Elect Engn Huwei 632 Yunlin Taiwan

In recent years, facial emotion recognition (FER) has been a popular topic in affective computing. However, FER still faces many challenges in automatic recognition for several reasons, including quality control of sample data, extraction of effective features, creation of models, and multi-feature fusion, which have not been thoroughly researched and therefore are still hot topics in computer visualization. In view of the mature development of deep learning, deep learning methods are increasingly being used in FER. However, because deep learning requires a large amount of data to achieve effective training, many studies have employed transfer learning to compensate for this drawback. Nevertheless, there has been no universal approach for transfer learning in FER. Accordingly, this study used the five classic models in FER (i.e., ResNet-50, Xception, EfficientNet-B0, Inception, and DenseNet121) to conduct a series of experiments: data preprocessing, training type, and the applicability of multi-stage pretraining. According to the results, class wight was the optimal technique for data balance. In addition, the freeze + fine-tuning training type can produce higher accuracy, regardless of the size of the dataset. Multi-stage training was also effective. Compared with the model accuracy in previous studies, the accuracy achieved in this study using the proposed transfer learning method was superior for both large and small datasets. Specifically, on affectnet, the accuracy for the ResNet-50, Xception, EfficientNet-B0, Inception, and DenseNet-121 models increased by 8.37%, 10.45%, 10.45%, 8.55%, and 5.47%, respectively. On FER2013, the accuracy for these models increased by 5.72%, 2%, 10.45%, 5%, and 9%, respectively. These results proved the validity and advantages of the experiments in this study.

关键词： Transfer learning convolutional neural network facial emotion recognition (FER) affective computing pretrained models fine-tuning FER2013 affectnet

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Emotion recognition from facial images with simultaneous occlusion, pose and illumination variations using meta-learning

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第9期34卷 7271-7282页

作者： Kuruvayil, Soumya Palaniswamy, Suja Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Bengaluru 560035 Karnataka India

Automatic facial emotion recognition in real-world situations like partial occlusions, varying head poses and illumination conditions are challenging to the machine learning community. The main reason is the lack of sufficient samples with the aforementioned conditions in the baseline datasets which throws difficulty in training a well performing machine learning or deep learning model. To overcome this challenge, we have adopted the concept of meta-learning. Meta-learning using prototypical networks (metric-based meta-learning) has been proven to be well-fit for few-shot problems without severe overfitting. We leverage the quick adaptation power of prototypical networks for emotion recognition in the scarcity of such diverse samples. We have used CMU Multi-PIE dataset which contains images with partial occlusions, varying head-poses and illumination levels for training and evaluating the model. For testing the adaptability of the system to intra-class and inter-dataset variations, affectnet face database images have been used. The proposed method is named as ERMOPI (Emotion Recognition using Metalearning across Occlusion, Pose and Illumination) which performs emotion recognition from facial expressions using meta-learning approach for still images and it is robust to partial occlusions, varying head poses and illumination levels which is the novelty of this work. The key benefit is the usage of less number of training samples compared to the existing work in emotion recognition and achieved comparable results with the state-of-the-art approaches. The proposed method achieved 90% accuracy for CMU Multi-PIE database images and 68% accuracy for affectnet database images. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Emotion recognition Meta -learning Prototypical networks Occlusion affectnet

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Automatic Emotion Detection as a Teaching Aid in Online Knowledge Assessment 20

Automatic Emotion Detection as a Teaching Aid in Online Know...

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20th International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Ilic, Veljko Batic, Dorde Mirkovic, Milan Vukmirovic, Srdan Culibrk, Dubravko Bosakov, Gavrilo Popovic, Ilija Fac Tech Sci Novi Sad Serbia Cinteract DOO Novi Sad Serbia

ISBN: (纸本)9781728182292

In this paper, we present an initial study of possibilities of applying Artificial Intelligence (AI) and computer vision-based approaches aimed to improve and alleviate the process of conducting knowledge assessment over the Microsoft Teams platform. We did that by developing a deep-neural-network-based system which is able to locate faces and predict emotions based on students' facial expressions. The system was evaluated on videos recorded during an online assessment of the ability of students to train and deploy deep learning solutions using Python and TensorFlow. We present results of this evaluation and show that, although the accuracy of our algorithm is limited at frame level, as we optimized for computational performance, it provides sufficient information to identify key changes on students' behavior, which should be brought to the teachers' attention.

关键词： Automatic emotion detection Online teaching Facial Expressions Deep Learning affectnet

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