Open AccessFacial Expression Recognition Based on Transfer Learning and SVM
Author(s) -
Yang Lei,
Haiqing Zhang,
Daiwei Li,
Fei Xiao,
Shanglin Yang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2025/1/012015
Subject(s) - support vector machine , computer science , convolutional neural network , transfer of learning , pattern recognition (psychology) , artificial intelligence , facial expression , data set , facial expression recognition , feature extraction , feature (linguistics) , facial recognition system , expression (computer science) , set (abstract data type) , machine learning , speech recognition , linguistics , philosophy , programming language
The facial expression datasets always have a problem: data with small amount or large amounts of data but also with large noisy. Both problems will affect the facial expression recognition accuracy of the model. A transfer learning method for facial expression recognition is proposed by combining the Convolutional Neural Network (CNN) and Support Vector Machine (SVM). SVM have good performance on small data sets and CNN based on transfer learning have better ability of feature extraction for large noisy data set. This method reduces the training time of model and increase the facial expression recognition accuracy. The experimental results show that the accuracy of the proposed method on the CK+ and FER2013 data sets has reached 99.6% and 68.1%.