Automatic social signal analysis: Facial expression recognition using difference convolution neural network

Chen, Jingying; Lv, Yongqiang; Xu, Ruyi; Xu, Can

doi:10.1016/j.jpdc.2019.04.017

Cited by 36 publications

(19 citation statements)

References 21 publications

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“…This specific area involves systems to classify the fundamental human emotions with current artificial intelligence algorithms, particularly neural networks FACS [20]. The FER general architecture comprises three phases: preprocessing, extraction of features, and classification [14], [21], [22].…”

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

“…For example, the SDM points to the facial organ shape changes and the features of texture like Local Directional Ternary Pattern (LDTP), Histogram of Oriented Gradients (HOG), Spatio-Temporal Texture Map (STTM), and LBP, to define changes in facial organ texture. Several works use the characteristics to clarify the differences in shape and texture [14]. The extraction characteristics were then used to classify the face expression [38].…”

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

“…During the classification point, different methods are applied to train the classifier using the extracted functions. [14]. Deep learning (DL) algorithms have recently been seen to be an effective technique in the field of image classification [29], [39].…”

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

“…A series of architectures of deep neural networks such as CNN's, Recurrent Neural Networks (RNNs), and Deep Neural Networks (DNNs) have been studied to help identify face emotions [30]. This system uses an end-to-end model to train a deep network structure with millions of parameters that automatically learn accurate features from massive data without the hand-made features [14], [40]. The in-depth features or activations may input conventional classification schemes [34], [37], [41].…”

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

“…However, the affective status of the pupil can be instantly interpreted by FER in the area of schooling. It lets teachers recognize their students' academic interests to include appropriate instructional methods to increase teaching effectiveness [14]. It is essential to monitor for some time the process of analyzing people's gestures, such as visitors who enjoy exhibitions in museums and have a method of thinking and exploring what they see.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Abdullah

Abdulazeez

2021

JSCDM

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Facial emotional processing is one of the most important activities in effective calculations, engagement with people and computers, machine vision, video game testing, and consumer research. Facial expressions are a form of nonverbal communication, as they reveal a person's inner feelings and emotions. Extensive attention to Facial Expression Recognition (FER) has recently been received as facial expressions are considered. As the fastest communication medium of any kind of information. Facial expression recognition gives a better understanding of a person's thoughts or views and analyzes them with the currently trending deep learning methods. Accuracy rate sharply compared to traditional state-of-the-art systems. This article provides a brief overview of the different FER fields of application and publicly accessible databases used in FER and studies the latest and current reviews in FER using Convolution Neural Network (CNN) algorithms. Finally, it is observed that everyone reached good results, especially in terms of accuracy, with different rates, and using different data sets, which impacts the results.

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