Research on facial emotion recognition model based on alpha-like algorithm and CNN fusion technology

This paper studied a facial emotion recognition model based on the fusion technology of Alpha algorithm and Convolutional Neural Network. By combining the spatial feature extraction ability of CNN with the advantages of alpha algorithm in sequence modeling, the performance of emotion recognition model was improved. This paper introduces the theoretical basis of deep learning and reinforcement learning, and proposes a model combining CNN and Alpha-like algorithm. The experimental results show that the accuracy of the fusion model is improved by about 2.1 times in the emotion classification task, especially in the recognition of anger and disgust, which is significantly higher than the traditional algorithm. However, it is also pointed out that the misclassification problem of the model on some complex emotion categories such as surprise and neutral still exists, and the performance of the model needs to be further improved in the future.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-02-17
DOI
https://doi.org/10.1051/itmconf/20257302001/pdf
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
0.00
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article

Research on facial emotion recognition model based on alpha-like algorithm and CNN fusion technology

Hongren Wang
Springer Link (Chiba Institute of Technology)
Emotion and Mood Recognition
article

Research on facial emotion recognition model based on alpha-like algorithm and CNN fusion technology

Hongren Wang
article en

Abstract

This paper studied a facial emotion recognition model based on the fusion technology of Alpha algorithm and Convolutional Neural Network. By combining the spatial feature extraction ability of CNN with the advantages of alpha algorithm in sequence modeling, the performance of emotion recognition model was improved. This paper introduces the theoretical basis of deep learning and reinforcement learning, and proposes a model combining CNN and Alpha-like algorithm. The experimental results show that the accuracy of the fusion model is improved by about 2.1 times in the emotion classification task, especially in the recognition of anger and disgust, which is significantly higher than the traditional algorithm. However, it is also pointed out that the misclassification problem of the model on some complex emotion categories such as surprise and neutral still exists, and the performance of the model needs to be further improved in the future.

Springer Link (Chiba Institute of Technology)
Peace, Justice and strong institutions
Openalex Percentile: Top 22%
Emotion and Mood Recognition
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