Results 21 to 30 of about 51,113 (306)
Face expression image detection and recognition based on big data technology
International Journal of Intelligent Networks, 2023 This research addresses the deficiencies in current dynamic sequence facial expression recognition methods, which suffer from limited accuracy and effectiveness.
Shuji Deng
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RRA-InceptionV3 Combined Robust Sparse Representation Method for Expression Recognition [PDF]
Jisuanji gongcheng, 2023 Facial expression recognition technology is an important subject in the field of computer vision.However, in real application, partial occlusion can lead to a sharp drop in the accuracy of facial expression recognition.In view of the low accuracy of ...
Hong XIE, Wengang JIANG
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Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM
Applied Sciences, 2022 Facial expression recognition is very useful for effective human–computer interaction, robot interfaces, and emotion-aware smart agent systems. This paper presents a new framework for facial expression recognition by using a hybrid model: a combination ...
Jin-Chul Kim +4 more
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Evolutionary Facial Expression Recognition [PDF]
Conference of the South African Institute of Computer Scientists and Information Technologists 2020, 2020 Deep facial expression recognition faces two challenges that both stem from the large number of trainable parameters: long training times and a lack of interpretability. We propose a novel method based on evolutionary algorithms, that deals with both challenges by massively reducing the number of trainable parameters, whilst simultaneously retaining ...
Emmanuel Dufourq, Bruce A. Bassett
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Facial Expression Recognition: A Survey [PDF]
Symmetry, 2019 Facial Expression Recognition (FER), as the primary processing method for non-verbal intentions, is an important and promising field of computer vision and artificial intelligence, and one of the subject areas of symmetry. This survey is a comprehensive and structured overview of recent advances in FER. We first categorise the existing FER methods into
Yunxin Huang +3 more
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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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NAO-Facial-Expression-Recognition
, 2021 This project includes a CNN-based FER model designed for real-world facial expression recognition. The “main.py” script allows you to integrate the model into NAO robot image’ streaming, under its Windows Python SDK (Naoqi).
Chiara Filippini (11480128)
core +1 more source
Covariance Pooling for Facial Expression Recognition [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018 Classifying facial expressions into different categories requires capturing regional distortions of facial landmarks. We believe that second-order statistics such as covariance is better able to capture such distortions in regional facial fea- tures. In this work, we explore the benefits of using a man- ifold network structure for covariance pooling to
Dinesh Acharya 0001 +3 more
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Coherence constraints in facial expression recognition [PDF]
Intelligenza Artificiale, 2019 This paper investigates the role of coherence constraints in recognizing facial expressions from images and video sequences. A set of constraints are introduced to bridge a pool of Convolutional Neural Networks (CNNs) during their training stage. Constraints are inspired by practical considerations on the regularity of the temporal evolution of the ...
Graziani L., Melacci S., Gori M.
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Deep facial expression recognition: a survey [PDF]
Journal of Image and Graphics, 2020 With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER.
Shan Li 0001, Weihong Deng
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