<title>Constructive Hermite polynomial feedforward neural networks with application to facial expression recognition</title>

During the COVID-19 pandemic, many offline activities are turned into online activities via video meetings to prevent the spread of the COVID 19 virus. In the online video meeting, some micro-interactions are missing when compared to direct social interactions. The use of machines to assist facial expression recognition in online video meetings is expected to increase understanding of the interactions among users. Many studies have shown that CNN-based neural networks are quite effective and accurate in image classification. In this study, some open facial expression datasets were used to train CNN-based neural networks with a total number of training data of 342,497 images. This study gets the best results using ResNet-50 architecture with Mish activation function and Accuracy Booster Plus block. This architecture is trained using the Ranger and Gradient Centralization optimization method for 60000 steps with a batch size of 256. The best results from the training result in accuracy of AffectNet validation data of 0.5972, FERPlus validation data of 0.8636, FERPlus test data of 0.8488, and RAF-DB test data of 0.8879. From this study, the proposed method outperformed plain ResNet in all test scenarios without transfer learning, and there is a potential for better performance with the pre-training model. The code is available at https://github.com/yusufrahadika-facial-expressions-essay.

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A SYSTEMATIC REVIEW OF METHODS OF EMOTION RECOGNITION BY FACIAL EXPRESSIONS

International Journal of Advanced Research ◽

10.21474/ijar01/12951 ◽

2021 ◽

Vol 9 (5) ◽

pp. 1141-1152

Author(s):

Muazu Abdulwakil Auma ◽

Eric Manzi ◽

Jibril Aminu

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Facial Expression ◽

Facial Expressions ◽

Facial Expression Recognition ◽

Deep Neural Networks ◽

Scientific Research ◽

Expression Recognition ◽

Technical Literature ◽

Research Results

Facial recognition is integral and essential in todays society, and the recognition of emotions based on facial expressions is already becoming more usual. This paper analytically provides an overview of the databases of video data of facial expressions and several approaches to recognizing emotions by facial expressions by including the three main image analysis stages, which are pre-processing, feature extraction, and classification. The paper presents approaches based on deep learning using deep neural networks and traditional means to recognizing human emotions based on visual facial features. The current results of some existing algorithms are presented. When reviewing scientific and technical literature, the focus was mainly on sources containing theoretical and research information of the methods under consideration and comparing traditional techniques and methods based on deep neural networks supported by experimental research. An analysis of scientific and technical literature describing methods and algorithms for analyzing and recognizing facial expressions and world scientific research results has shown that traditional methods of classifying facial expressions are inferior in speed and accuracy to artificial neural networks. This reviews main contributions provide a general understanding of modern approaches to facial expression recognition, which will allow new researchers to understand the main components and trends in facial expression recognition. A comparison of world scientific research results has shown that the combination of traditional approaches and approaches based on deep neural networks show better classification accuracy. However, the best classification methods are artificial neural networks.

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