相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Engineering, Electrical & Electronic
Dongliang Chen et al.
Summary: This paper proposes a novel facial expression recognition framework called multi-relations aware network (MRAN), which can focus on global and local attention features and learn the multi-level relationships among local regions, between global-local features and among different samples, to obtain efficient emotional features. Experimental results show that our method obtains superior performance compared to some state-of-the-art models on in-the-wild databases.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Jae Young Choi et al.
Summary: Although emotion recognition methods based on facial expression cues perform well in controlled environments, recognizing emotion in the wild is still challenging due to various factors. This paper introduces a novel ensemble deep learning method, DCNN ensemble classifier, for improved facial expression recognition in the wild. The proposed method combines different types of face representations and utilizes bagging to increase the diversity of the ensemble, achieving competitive performance on three wild FER datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Multidisciplinary Sciences
Houqiu Long et al.
Summary: Facial expressions are important for communication between social agents. The study developed the Wild Faces Database (WFD), which contains images of diverse facial behaviors captured outside of the laboratory. Participants categorized the emotions in the images and rated the intensity and genuineness. The WFD captured a range of emotional expressions, suggesting that natural expressions are more multiplexed than a categorical model would predict.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Gan Chen et al.
Summary: Facial expression recognition has gained extensive attention recently due to its important applications in various fields. However, in certain situations, emotions are expressed in suppressed or subtle ways, making it difficult to accurately estimate them. To address this problem, an end-to-end group adaptive attention model for subtle expression recognition is proposed.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Fuyan Ma et al.
Summary: Facial Expression Recognition (FER) in the wild is challenging due to various factors, but the proposed Visual Transformers with Feature Fusion (VTFF) approach demonstrates superior performance by leveraging attentional selective fusion (ASF) and modeling relationships with global self-attention. Extensive experiments on three facial expression datasets show that VTFF sets new state-of-the-art results. The method also exhibits promising generalization capability on cross-dataset evaluation.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Mohan Karnati et al.
Summary: Emotion recognition is important in cognitive psychology research, but measuring emotions is challenging. Facial expression recognition (FER) approaches have been designed, but challenges increase when data transitions to real-world circumstances. Deep learning (DL) techniques have greatly improved FER systems, but problems such as overfitting and complications unrelated to expressions still exist. This study provides a comprehensive survey of DL-based FER methods, discussing different components and analyzing their performance, advantages, and limitations. It also explores relevant databases and discusses the current status and future directions of facial emotion recognition.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Carmen Bisogni et al.
Summary: This article proposes a facial expression recognition system that can provide quick assistance to the healthcare system and exceptional services to the patients. The system utilizes multi-resolution image processing techniques and has been proven superior through experiments.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Muhamad Dwisnanto Putro et al.
Summary: This article proposes an efficient CPU-based facial expression detector using a sequential attention network to improve performance. The proposed model achieves high accuracy and competitive performance compared to state-of-the-art methods. It is also integrated with a face detector for real-time execution on a CPU.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Multidisciplinary
Avinandan Banerjee et al.
Summary: The paper introduces a robust deep learning ensemble framework called COFE-Net for COVID-19 screening from chest X-rays and CT scans in Computer-Aided Detection (CADe). By leveraging fuzzy logic principles and Transfer Learning, the proposed method effectively combines decision scores from three state-of-the-art CNNs, showing superiority over empirical ensemble methods. The dynamic refactoring of classifier ensemble weights based on confidence scores for coalition of inputs is the chief advantage of the proposed biomedical measurement strategy.
Article
Computer Science, Artificial Intelligence
Yingruo Fan et al.
Summary: This article introduces a deeply-supervised attention network (DSAN) for facial expression recognition which overcomes the limitations caused by demographic variation. The DSAN framework incorporates multi-scale features and attention blocks to capture more discriminative information, and combines convolutional layer features in a deeply-supervised manner. Experimental results demonstrate the superiority of the proposed framework on facial expression databases with varied gender, age, and race.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Mohan Karnati et al.
Summary: The COVID-19 pandemic has posed an unprecedented threat to the global public health system. Researchers have developed an end-to-end IoT infrastructure to remotely diagnose patients and measure the severity of COVID-19 lung infections. The proposed deep neural network model outperforms baseline techniques in experimental tests on multiple databases. The application of this model can evaluate treatment efficacy, particularly in remote locations.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Electrical & Electronic
Hanwei Liu et al.
Summary: This paper proposes an adaptive multilayer perceptual attention network (AMP-Net) for facial expression recognition in complex real-world situations. AMP-Net extracts facial emotional features with different fine-grained features and improves robustness and generalizability.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Mohan Karnati et al.
Summary: In this study, a novel deep convolutional neural network (DCNN) named LieNet is proposed to automatically detect multiscale variations of deception. LieNet combines contact and noncontact-based approaches, extracting features from video frames, audio signals, and electroencephalogram signals to estimate scores that support either deception or truth. Experimental results show that LieNet achieves high accuracy in deception detection.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yifan Xia et al.
Summary: Research on facial expression recognition in the wild is challenging due to pose, occlusion, illumination, and intensity variations. This article proposes a relation-aware facial expression recognition method that captures the relationship between crucial regions and facial expressions, leading to improved recognition accuracy.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Mohan Karnati et al.
Summary: With the development of deep learning, the research on facial expression recognition (FER) has gained significant attention. This study proposes a texture-based feature-level ensemble parallel network (FLEPNet) to address the challenges in FER, and experimental results demonstrate its effectiveness and reliability.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Andrey V. V. Savchenko et al.
Summary: This article analyzes the behavior of students in the e-learning environment and proposes a novel pipeline based on video facial processing. By applying face detection, tracking, and clustering techniques, sequences of faces of each student are extracted, and an efficient neural network is used to extract emotional features. The resulting facial features enable fast simultaneous prediction of students' engagement levels, individual emotions, and group-level affect. Additionally, the possibility of creating a summary of a lesson by saving short clips of different emotions and engagement is demonstrated.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Hai-Duong Nguyen et al.
Summary: Emotion recognition is crucial in human-machine interaction systems, but sampling in real-life scenarios remains challenging. This study proposes a two-stage method for facial expression recognition and improves the classification task by introducing multi-level features and various network connections. Experimental results demonstrate the superior performance of the proposed method on the dataset.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Karnati Mohan et al.
Summary: Automatic facial expression recognition is a challenging task in computer vision with applications in human-computer interaction and behavioral psychology. Previous works have focused on handcrafted features, but accurately extracting all relevant features is difficult due to emotional state variations. This study proposes FER-net, a convolutional neural network, and demonstrates its superiority over twenty-one state-of-the-art methods on multiple benchmark datasets by achieving high accuracy rates for distinguishing seven facial expressions.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Automation & Control Systems
Zhenghao Xi et al.
Summary: This study proposes a parallel neural network combining texture features to improve facial expression recognition, achieving a high accuracy of 98.14% compared to ResNet. The results demonstrate the effectiveness and robustness of the proposed method in facial expression classification.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Automation & Control Systems
Min Wu et al.
Summary: The weight-adapted convolutional neural network (WACNN) is proposed to extract discriminative representations of facial expressions, achieving deep and global emotion understanding in human-robot interaction. Experimental results demonstrate superior recognition accuracy compared to state-of-the-art methods, and preliminary application experiments show that wheeled robots can recognize basic expressions in an emotional social robot system.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Avinandan Banerjee et al.
Summary: The paper introduces an action recognition method based on skeleton key joints, utilizing four feature representations to improve classification effectiveness, and adaptively generates final decision score via fuzzy fusion.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Karnati Mohan et al.
Summary: This work proposes a deep-learning-based scheme for identifying facial expressions, exploring both local and holistic features and using score-level fusion to compute the final classification score. The method outperformed all state-of-the-art methods on five benchmark databases.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Computer Science, Artificial Intelligence
Zengqun Zhao et al.
Summary: This paper proposed a global multi-scale and local attention network for facial expression recognition in the wild, achieving state-of-the-art results on several benchmarks.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Information Systems
Wei Huang et al.
Summary: Person-dependent facial expression recognition has been a focus of research, with identity differences affecting recognition accuracy. This study proposes a novel identity-aware method using deep metric learning and facial image synthesis techniques for challenging person-dependent facial expression recognition.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Computer Science, Artificial Intelligence
Ming Li et al.
Summary: This study introduces a deep learning approach that combines identity features and emotional features to enhance facial expression recognition tasks. Experimental results demonstrate high accuracy rates on different databases, outperforming baseline methods and state-of-the-art techniques.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Fatemeh Noroozi et al.
Summary: Automatic emotion recognition, particularly from body gestures, is a trending research topic that has received less exploration compared to facial expressions or speech-based recognition. Researchers introduced a framework for recognizing emotional body gestures and discussed limitations of current methods.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2021)
Article
Computer Science, Information Systems
Hao Yang et al.
Summary: Driver fatigue is identified as the main cause of traffic accidents. This study proposes a novel approach for detecting yawning based on subtle facial action recognition, using a 3D deep learning network and a keyframe selection algorithm to improve detection accuracy.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Computer Science, Artificial Intelligence
Jing Li et al.
Article
Computer Science, Artificial Intelligence
Kai Wang et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2020)
Article
Computer Science, Information Systems
Guihua Wen et al.
IEEE TRANSACTIONS ON MULTIMEDIA
(2020)
Article
Computer Science, Interdisciplinary Applications
Ardhendu Behera et al.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION
(2020)
Article
Computer Science, Information Systems
Siyue Xie et al.
IEEE TRANSACTIONS ON MULTIMEDIA
(2019)
Article
Computer Science, Artificial Intelligence
Yong Li et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2019)
Article
Computer Science, Artificial Intelligence
Shan Li et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2019)
Article
Computer Science, Artificial Intelligence
Mahbubul Alam et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Huiyuan Yang et al.
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2018)
Article
Multidisciplinary Sciences
Martin Wegrzyn et al.
Article
Computer Science, Artificial Intelligence
Changxing Ding et al.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2016)
Article
Computer Science, Artificial Intelligence
Evangelos Sariyanidi et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2015)
Article
Computer Science, Artificial Intelligence
Shengcai Liao et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2013)
Article
Computer Science, Artificial Intelligence
Chun-Nian Fan et al.
PATTERN RECOGNITION LETTERS
(2011)
