Facial expression recognition based on multi-channel fusion and lightweight neural network

In the process of facial expression recognition, face detection is the prerequisite, image preprocessing is the foundation, facial expression feature extraction is the key, and facial expression classification is the target. Effective feature extraction in this process can improve the accuracy of fa...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 27; číslo 24; s. 18549 - 18563
Hlavní autoři: Yu, Yali, Huo, Hua, Liu, Junqiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023
Springer Nature B.V
Témata:
Accuracy
Algorithms
Artificial Intelligence
Classification
Communication
Computational Intelligence
Control
Data Analytics and Machine Learning
Deep learning
Edge detection
Engineering
Face recognition
Feature extraction
Interdisciplinary subjects
Machine learning
Mathematical Logic and Foundations
Mechatronics
Neural networks
Robotics
Support vector machines
Texture
Wavelet transforms
Canny edge detector
Facial expression recognition
Face detection
Lightweight neural network
Local binary patterns
ISSN:1432-7643, 1433-7479
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Popis
Shrnutí:In the process of facial expression recognition, face detection is the prerequisite, image preprocessing is the foundation, facial expression feature extraction is the key, and facial expression classification is the target. Effective feature extraction in this process can improve the accuracy of facial expression classifications. On the other hand, traditional facial expression recognition methods are not only complicated in the feature extraction process, but also unable to obtain more in-depth high-semantic features and deep features from the original image. To solve the above problems, this paper proposes a facial expression recognition method based on multi-channel fusion and lightweight neural network. First, a cascade classifier based on Haar features is used to detect the face region of the facial expression image. Second, the local binary pattern (LBP) is used to extract the local texture features from the face region. Third, face edge features are simultaneously obtained by performing edge detection in the face region based on the Canny edge detection algorithm. Fourth, the obtained face image, LBP texture feature image, and edge detection Canny image are fused, and the fused image is input into the constructed lightweight neural network for training and recognition. Experiments are carried out on the public image databases Facial Expression Recognition 2013 (Fer2013) and extended Cohn–Kanade (CK +) using the hold-out cross-validation method. The experimental results show that the proposed method effectively extracts more complete image features by combining traditional feature extraction algorithms with deep learning feature extraction algorithms, improves the accuracy and robustness of facial expression recognition, and has better recognition rate and generalization ability compared to other mainstream methods.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-09199-1