Facial Expression Recognition Based on Improved AlexNet

Xu, 杨旭 Yang; Zhenhong, 尚振宏 Shang

doi:10.3788/lop57.141026

Cited by 8 publications

(2 citation statements)

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“…Researchers have pioneered various enduring network architectures using deep learning, including renowned ones like AlexNet, VGGNet, and GoogLeNet. In [9], an enhanced approach involving multi-channel convolution, global average pooling, and batch normalization was incorporated into the AlexNet network for facial expression recognition, leading to a remarkable 13.24% boost in accuracy compared to the original network configuration. Similarly in [10], authors employed an expression recognition methodology rooted in VGGNet, achieving an enhanced expression recognition rate.…”

Section: Research Of Facial Expression Recognitionmentioning

confidence: 99%

Enhancing Facial Expression Recognition System in Online Learning Context Using Efficient Deep Learning Model

Aly,

Ghallab,

Fathi

2023

IEEE Access

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This paper presents an online educational platform that leverages facial expression recognition technology to monitor students' progress within the classroom. Periodically, a camera captures images of students in the classroom, processes these images, and extracts facial data through detection methods. Subsequently, students' learning statuses are assessed using expression recognition techniques. The developed approach then dynamically refines and enhances teaching strategies using the acquired learning status data. In the course of the experiment, we enhance facial expression recognition accuracy through the utilization of ResNet-50 for effective feature extraction. Additionally, by adjusting the residual down-sampling module, we bolster the correlation among input features, thus mitigating the loss of feature information. Simultaneously, a convolutional attention mechanism module is incorporated to reduce the influence of irrelevant areas within the feature map. The proposed method achieves an accuracy of 87.62% and 88.13 % on the RAF-DB and FER2013 expression datasets, respectively. In comparison with the original ResNet-50 network and the expression recognition outcomes found in existing literature, the suggested approach demonstrates enhanced accuracy and improved detection of students' learning states and expression variations. Consequently, the application of facial expression recognition technology in online learning, along with the optimization of online teaching resources and strategies grounded in th e results of recognition, holds tangible value for augmenting the quality of online learning experiences . We have benchmarked the proposed model against state-of-the-art techniques and conducted evaluations using the FER-2013, CK+, and KDEF datasets. The s ignificance of these results lies in their potential application within educational institutions.

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Section: Research Of Facial Expression Recognitionmentioning

confidence: 99%

Enhancing Facial Expression Recognition System in Online Learning Context Using Efficient Deep Learning Model

Aly,

Ghallab,

Fathi

2023

IEEE Access

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“…Literature [10] takes the lightweight network GhostNet as the backbone network, and combines the proposed deep and shallow feature fusion structure, channel attention module, and multi-scale spatial attention module to make them become a mutually reinforcing whole, thus improving the accuracy and generalization of facial expression recognition. Literature [11] introduces multiscale convolution, cross-connection and global average pooling methods based on AlexNet to improve and optimize the network. The proposed improved AlexNet algorithm has a certain improvement in the accuracy of expression recognition, but there is still a problem of low recognition accuracy for a certain expression.…”

Section: Introductionmentioning

confidence: 99%

Facial expression recognition with convolutional neural network embedded with attention mechanism

Wang,

Abisado

2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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Facial expression recognition has always been a complex problem in computer vision. In recent years, with the rapid development of deep learning, some methods based on convolutional neural networks have greatly improved the accuracy of expression recognition. To further improve the accuracy of facial expression recognition, this paper proposes a convolutional neural network facial expression recognition model embedded with attention mechanism. The convolutional layer is used to extract the shallow features of facial expressions, the SE attention module is embedded into the network model to realize the extraction of salient regional features of facial expressions. To effectively prevent gradient explosion and gradient disappearance, and speed up the training and convergence speed of the network, a BN layer is added after each convolutional layer. The convolutional layer is used instead of the fully connected layer, and 1×1 convolution is used to achieve classification, which reduces the number of parameters, avoids the overfitting problem caused by a large amount of calculation, and improves the generalization ability of the model. The experimental results show that the facial expression recognition accuracy of the proposed method on the FER2013 and CK+ public datasets has been improved to a certain extent, which verifies the method's effectiveness.

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Expression recognition based on residual rectification convolution neural network

Chen

Zhu

Dong

2022

Multimed Tools Appl

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Facial Expression Recognition Based on Improved AlexNet

Cited by 8 publications

References 0 publications

Enhancing Facial Expression Recognition System in Online Learning Context Using Efficient Deep Learning Model

Enhancing Facial Expression Recognition System in Online Learning Context Using Efficient Deep Learning Model

Facial expression recognition with convolutional neural network embedded with attention mechanism

Expression recognition based on residual rectification convolution neural network

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