Facial Expression Recognition Algorithm Based on Convolution Neural Network and Multi-Feature Fusion

Tang, Xiaoping; Zhang, Tianping

doi:10.1088/1742-6596/1883/1/012018

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…These algorithms have gradually improved the training stability of GANs and the resolution of generated images, laying the foundation for their application in computer vision tasks. For instance, Deng et al applied WGAN‐GP to data augmentation for facial expression recognition, improving the accuracy of recognizing facial expressions from multiple angles (Gao et al, 2023). Li et al used CycleGAN to exchange textures and colors of three types of wood (poplar, birch, and pine), and generated images with cracks and worms, addressing the issue of imbalanced defect distributions and enhancing the accuracy of wood defect detection and segmentation (Li et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Detection of corn unsound kernels based on GAN sample enhancement and improved lightweight network

Zhu,

Wang,

et al. 2023

J Food Process Engineering

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The presence of corn unsound kernels, one of the world's major food crops, could have a significant impact on the food industry and the food supply. The traditional method of detecting unsound corn kernels by hand during grain acquisition has many drawbacks, and computer vision‐based detection methods have become mainstream. In this paper, a corn unsound kernel detection algorithm based on generative adversarial network (GAN) sample enhancement and an improved lightweight network is introduced. The article first builds a corn unsound kernel image acquisition platform and makes a dataset by preprocessing and segmenting the collected corn seed cluster images with the improved concave point segmentation algorithm. Then, to increase the diversity and number of datasets, the StyleGANv2 network was improved to generate corn‐unsound kernel images with diverse features. Finally, to meet the demand for lightweight detection, the MobileVit network was optimized to improve the network's recognition accuracy, which reached 96.2%. The article verifies the effectiveness and superiority of the proposed algorithm through experiments.

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Section: Methodsmentioning

confidence: 99%

Detection of corn unsound kernels based on GAN sample enhancement and improved lightweight network

Zhu,

Wang,

et al. 2023

J Food Process Engineering

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“…Through the high-dimensional nonlinear mapping relationship between the electrical quantity and the voltage stability state, the voltage stability of the distributed energy storage cloud group end region at the power grid side can be analyzed. The deep structure of the multi-scale and multi feature convolution neural network can obtain more information, which is more intelligent than artificial, and can obtain the key characteristics of the input quantity [10,11]. The multi-scale and multi feature convolutional neural network can describe the high-dimensional nonlinear mapping relationship in many aspects, and process the high-dimensional data with high quality by sharing the convolution kernel.…”

Section: Grid Side Distributed Energy Storage Cloud Group End Region ...mentioning

confidence: 99%

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based on Multi-Scale and Multi Feature Convolution Neural Network

Wen¹,

Zhu²,

Li³

et al. 2023

Energy Engineering

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There is instability in the distributed energy storage cloud group end region on the power grid side. In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components show a continuous and stable charging and discharging state, a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed. Firstly, a voltage stability analysis model based on multi-scale and multi feature convolution neural network is constructed, and the multi-scale and multi feature convolution neural network is optimized based on Self-Organizing Maps (SOM) algorithm to analyze the voltage stability of the cloud group end region of distributed energy storage on the grid side under the framework of credibility. According to the optimal scheduling objectives and network size, the distributed robust optimal configuration control model is solved under the framework of coordinated optimal scheduling at multiple time scales; Finally, the time series characteristics of regional power grid load and distributed generation are analyzed. According to the regional hierarchical time-sharing configuration model of "cloud", "group" and "end" layer, the grid side distributed energy storage cloud group end regional hierarchical time-sharing configuration algorithm is realized. The experimental results show that after applying this algorithm, the best grid side distributed energy storage configuration scheme can be determined, and the stability of grid side distributed energy storage cloud group end region layered timesharing configuration can be improved. KEYWORDSMultiscale and multi feature convolution neural network; distributed energy storage at grid side; cloud group end region; layered time-sharing configuration algorithm

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“…With the research of these researchers, it is found that combining the extracted features with the network parameters of CNN can cope with the recognition difficulties in complex environments. Wang, Tang et al (2020) used CNNs such as ResNet to extract features, and trained them with face recognition models to evaluate different faces based on the obtained threshold value. This experiment extracted features offline and iterated the training set 1000 times, gaining the recognition accuracy of more than 98.2%.…”

Section: Extract Effective Face Attributesmentioning

confidence: 99%

Toward masked face recognition: An effective facial feature extraction and refinement model in multiple scenes

et al. 2022

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With the impact of the COVID‐19 epidemic, the demand for masked face recognition technology has increased. In the process of masked face recognition, some problems such as less feature information and poor robustness to the environment are obvious. The current masked face recognition model is not quantified enough for feature extraction, there are large errors for faces with high similarity, and the categories cannot be clustered during the detection process, resulting in poor classification of masks, which cannot be well adapted to changes in multiple environments. To solve current problems, this paper designs a new masked face recognition model, taking improved Single Shot Multibox Detector (SSD) model as a face detector, and replaces the input layer VGG16 of SSD with Deep Residual Network (ResNet) to increase the receptive field. In order to better adapt to the network, we adjust the convolution kernel size of ResNet. In addition, we fine‐tune the Xception network by designing a new fully connected layer, and reduce the training cycle. The weights of the three input samples including anchor, positive and negative are shared and clustered together with triplet network to improve recognition accuracy. Meanwhile, this paper adjusts alpha parameter in triplet loss. A higher value of alpha can improve the accuracy of model recognition. We further adopt a small trick to classify and predict face feature vectors using multi‐layer perceptron (MLP), and a total of 60 neural nodes are set in the three neural layers of MLP to get higher classification accuracy. Moreover, three datasets of MFDD, RMFRD and SMFRD are fused to obtain high‐quality images in different scenes, and we also add data augmentation and face alignment methods for processing, effectively reducing the interference of the external environment in the process of model recognition. According to the experimental results, the accuracy of masked face recognition reaches 98.3%, it achieves better results compared with other mainstream models. In addition, the hyper‐parameters tuning experiment is carried out to improve the utilization of computing resources, which shows better results than the indicators of different networks.

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Facial Expression Recognition Algorithm Based on Convolution Neural Network and Multi-Feature Fusion

Cited by 3 publications

References 1 publication

Detection of corn unsound kernels based on GAN sample enhancement and improved lightweight network

Detection of corn unsound kernels based on GAN sample enhancement and improved lightweight network

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based on Multi-Scale and Multi Feature Convolution Neural Network

Toward masked face recognition: An effective facial feature extraction and refinement model in multiple scenes

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