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Facial Expression Recognition [PDF]
Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning), 2019 The purpose of this project was to analyze which image pre-processing technique was most beneficial in improving the performance of Facial Expression Recognition through Deep Learning and High-Performance Computing. Contrary to our expectations, the results obtained in this work showed that deep learning does not significantly benefit from various ...
Francisco Reveriano +2 more
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Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition [PDF]
IEEE Transactions on Image Processing, 2019 Occlusion and pose variations, which can change facial appearance significantly, are two major obstacles for automatic Facial Expression Recognition (FER). Though automatic FER has made substantial progresses in the past few decades, occlusion-robust and
K. Wang +4 more
semanticscholar +1 more source
Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild [PDF]
AAAI Conference on Artificial Intelligence, 2022 Compared with the image-based static facial expression recognition (SFER) task, the dynamic facial expression recognition (DFER) task based on video sequences is closer to the natural expression recognition scene. However, DFER is often more challenging.
Hanting Li +3 more
semanticscholar +1 more source
IEEE Access, 2020 The recognition rate of person-independent facial expression is generally not high, which limits the practical application of facial expression recognition.
Ying He, Shuxin Chen
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Robust Lightweight Facial Expression Recognition Network with Label Distribution Training
AAAI Conference on Artificial Intelligence, 2021 This paper presents an efficiently robust facial expression recognition (FER) network, named EfficientFace, which holds much fewer parameters but more robust to the FER in the wild.
Zengqun Zhao, Qingshan Liu, Feng Zhou
semanticscholar +1 more source
Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin [PDF]
Computer Vision and Pattern Recognition, 2022 Only parts of unlabeled data are selected to train models for most semi-supervised learning methods, whose confidence scores are usually higher than the pre-defined threshold (i.e., the confidence margin). We argue that the recognition performance should
Hangyu Li +4 more
semanticscholar +1 more source
IEEE Access, 2023 Aiming at the problem of insufficient feature extraction and low recognition rate of traditional convolutional neural network in facial expression recognition, a multi-layer feature recognition algorithm based on three-channel convolutional neural ...
Ying He +3 more
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A facial expression recognition method based on face texture feature fusion
Journal of Hebei University of Science and Technology, 2021 Aiming at facial expression recognition, the recognition rate is not high due to noise and occlusion. A hybrid approach of facial expression has been presented by combining local and global features.
Tingting GAO, Hang LI, Shoulin YIN
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Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network [PDF]
Italian National Conference on Sensors, 2019 Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation.
Shervin Minaee, AmirAli Abdolrashidi
semanticscholar +1 more source
Efficient recognition of facial expressions does not require motor simulation
eLife, 2020 What mechanisms underlie facial expression recognition? A popular hypothesis holds that efficient facial expression recognition cannot be achieved by visual analysis alone but additionally requires a mechanism of motor simulation — an unconscious, covert
Gilles Vannuscorps +2 more
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