Subject independent facial expression recognition with robust face detection using a convolutional neural network

By M. Matsugu, Katsuhiko Mori, Yusuke Mitari, Yuji Kaneda. Neural Networks, 2003

607 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6dd440eec6718f29a35b464345df2c77a4f0e085

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Citations in the corpus

Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines (2007, methodology): “A method that performs facial expression recognition is presented in [15].”
Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order (2017, methodology): “One of the techniques that has been successfully applied to the facial expression recognition problem was the deep multi-layer neural network [30, 31, 32, 14, 10, 11, 13].”
Deep into the Brain: Artificial Intelligence in Stroke Imaging (2017, background): “org would be better.(9) CNN nodes connect to each other in a geometrical structure, not showing all-to-all connections (Figure 3).”
Deep Learning and Its Applications in Biomedicine (2018, background): “This type of feature extraction was inspired by the visual mechanisms in living organisms, where cells in the visual cortex are sensitive to small regions of the visual ﬁeld [52,102].”
Learning to Spot and Refactor Inconsistent Method Names (2019, methodology): “Our approach uses CNNs [33] as the embedding function”
Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks (2020, background): “It considers maximum of the region from feature map obtained from convolution operator [30] .”
Multi-Classification of Brain Tumor Images Using Deep Neural Network (2019, methodology): “It is inspired by biological processes in human brain [17] and utilized to handle data that come in multiple arrays [18].”
Human action recognition using genetic algorithms and convolutional neural networks (2016, background): “recognition [19,4], video quality assessment [20] and action recognition [21–23].”
Deep Facial Expression Recognition: A Survey (2018, unknown): “re [98], [99] found that the CNN is robust to face location changes and scale variations and behaves better than the multilayer perceptron (MLP) in the case of previously unseen face pose variations. [100] employed the CNN to address the problems of subject independence as well as translation, rotat”
Artificial intelligence in the creative industries: a review (2020, background): “d, associating them to the characteristics of the training data. The early layers in a CNN extract low-level features conceptually similar to visual basis functions found in the primary visual cortex [317]. The most common CNN architecture (Fig. 11 (a)) has the outputs from its convolution layers co”
Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review (2020, unknown): “As an attempt to improve depth estimation, researchers in [82,83] trained a deep learning CNN network to estimate depth using a single monocular camera.”