Design and Implementation of Face Emotion Recognition System Based on CNN Mini_Xception Frameworks

Sun, Lili; Ge, Chenhao; Zhong, Yuanchang

doi:10.1088/1742-6596/2010/1/012123

Cited by 6 publications

(2 citation statements)

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“…Emotions, which consists of a series of neurophysiological changes, are fundamental to human decisionmaking and interaction processes. According to studies, only around 45% of human communication is spoken, with the other 55% mostly expressed through facial expressions of emotion [1]. Thus, understanding facial expressions will be a useful tool for non-verbal communication because they reveal people's complex and variable inner thoughts.…”

Section: Introductionmentioning

confidence: 99%

CNN combined with attention module for facial expression recognition

Yang

2023

J. Phys.: Conf. Ser.

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In contemporary times, the proliferation of information technology has prompted an increased interest among scholars towards artificial intelligence, particularly its constituent algorithms, such as deep learning, multilayer perceptron and convolutional neural networks. Of specific interest is the analysis of facial expressions, which has emerged as a popular research topic. However, classifying facial expressions presents a significant challenge due to variations in expressions associated with different emotions, as well as similarities between the various emotions. The task of expression classification is further complicated by the abundance of facial features that must be considered. In this paper, the basic Convolutional Neural Network (CNN) model was first trained on the FRE-2013 face expression dataset and optimized by adding an attention module to learn and analyse the key features of faces, thereby improving the classification accuracy of the model. In short, this model achieved an accuracy of 71.65% and an F1-Score of 0.66 while the model without the attention module only achieved a model accuracy of 69.86% and an F1-Score of 0.65, an improvement of about 2%. The analysis demonstrated that by adding the attention mechanism, some important features e.g. the eyes and mouth, are given more weight, thus improving the classification accuracy.

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Section: Introductionmentioning

confidence: 99%

CNN combined with attention module for facial expression recognition

Yang

2023

J. Phys.: Conf. Ser.

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“…The second neural network applies the transfer learning approach using the pretrained mini-Xception architecture initially trained on the FER2013 dataset. Some authors mention [27][28][29] that the mini-Xception architecture is specifically designed for tasks such as image classification, and its structure is as follows:…”

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confidence: 99%

Emotion Recognition in Individuals with Down Syndrome: A Convolutional Neural Network-Based Algorithm Proposal

2023

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This research introduces an algorithm that automatically detects five primary emotions in individuals with Down syndrome: happiness, anger, sadness, surprise, and neutrality. The study was conducted in a specialized institution dedicated to caring for individuals with Down syndrome, which allowed for collecting samples in uncontrolled environments and capturing spontaneous emotions. Collecting samples through facial images strictly followed a protocol approved by certified Ethics Committees in Ecuador and Colombia. The proposed system consists of three convolutional neural networks (CNNs). The first network analyzes facial microexpressions by assessing the intensity of action units associated with each emotion. The second network utilizes transfer learning based on the mini-Xception architecture, using the Dataset-DS, comprising images collected from individuals with Down syndrome as the validation dataset. Finally, these two networks are combined in a CNN network to enhance accuracy. The final CNN processes the information, resulting in an accuracy of 85.30% in emotion recognition. In addition, the algorithm was optimized by tuning specific hyperparameters of the network, leading to a 91.48% accuracy in emotion recognition accuracy, specifically for people with Down syndrome.

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