Facial Expression Recognition: A Review of Methods, Performances and Limitations

Ekundayo, Olufisayo; Viriri, Serestina

doi:10.1109/ictas.2019.8703619

Cited by 21 publications

(15 citation statements)

References 27 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%

“…The pieces of this section include facial orientation, gray image transfer, 2-D noise-removal adaptive filtering, image sharpening using sharp masking, and data increase [23]. In preprocessing, input data quality (image) is improved, and redundancy is reduced or eliminated [21]. Next, an image input RGB of m×n size is read and converted into a gray image with the standard equation [24].…”

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

“…The extracted data should provide valuable knowledge that could contribute to a proper estimation for the classification technique. The product of the extraction process affected the system's output [21]. The traditional algorithms for facial extraction can be divided into two categories: 1) geometric approaches such as Active Appearance Models (AAM); and 2) appearance-based methods like Gabor wavelet representation and Local Binary Pattern (LBP) [34]; in a geometric approach, various geometrical parameters such as position, angle, points of reference, etc.…”

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

“…• Classification: This is the final stage of the FER method, and the actual mapping for labeled emotion of action units happens here [21]. During the classification point, different methods are applied to train the classifier using the extracted functions.…”

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

See 3 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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Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

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

Abdullah

Abdulazeez

2021

JSCDM

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“…Emotion detection plays an important role in many areas including but not limited to intelligent security [1], robotics manufacturing [2], clinical psychology [3], multimedia [4], and automotive security [5]. Facial expression recognition (FER), which is an important research area of Human-Machine Interaction (HMI), is the task of detecting emotions by analyzing facial expressions that play a key role in social interaction [6] and convey meaningful and clear information about the emotions of people [7]. As a natural consequence of that, various computer vision systems based on machine learning algorithms have proposed FER where they were trained using annotated face datasets.…”

Section: Introductionmentioning

confidence: 99%

PyFER: A Facial Expression Recognizer Based on Convolutional Neural Networks

Kabakuş

2020

IEEE Access

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Facial expression recognition (FER), one of the most trending research areas of the Human-Machine Interaction, is the task of detecting emotions by analyzing facial expressions and this analysis plays a critical role as it conveys the clearest information regarding the emotions of people. Despite the fact that the traditional machine learning algorithms produce high accuracies for similar tasks, they lack to detect emotions of faces, which are captured in a spontaneous manner (a.k.a. "in the wild") or in different poses or environmental conditions. In this paper, a novel convolutional neural network architecture, namely, PyFER, is proposed to address the FER problem, of which the efficiency was revealed thanks to the experiments conducted on a widely-used benchmark dataset. According to the experimental results, the accuracy of PyFER was calculated to be as high as 96.3% on a de-facto standard dataset, namely, CK+, and all facial expressions, except for ℎ , were correctly detected by PyFER, which is encouraging for future studies. 16.67% of the images that actually represented the facial expression happiness were misdetected as the facial expression fear. The experimental results confirmed that the proposed neural network architecture is fast enough to be integrated into real-time FER applications as it was able to complete the analysis of a given photo for an average of 12.8 milliseconds, which is in the tolerable limit to latency for real-time applications.

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Face Emotion Recognition Based on Images Using the Haar-Cascade Front End Approach

Pushpa,

Kotti,

Bindumadhuri

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Facial Expression Recognition: A Review of Methods, Performances and Limitations

Cited by 21 publications

References 27 publications

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

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

PyFER: A Facial Expression Recognizer Based on Convolutional Neural Networks

Face Emotion Recognition Based on Images Using the Haar-Cascade Front End Approach

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