Micro-Expression Recognition by Using CNN Features with PSO Algorithm and SVM Methods
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Int Information & Engineering Technology Assoc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This study proposes a framework for defining ME expressions, in which preprocessing, feature extraction with deep learning, feature selection with an optimization algorithm, and classification methods are used. CASME-II, SMIC-HS, and SAMM, which are among the most used ME datasets in the literature, were combined to overcome the under-sampling problem caused by the datasets. In the preprocessing stage, onset, and apex frames in each video clip in datasets were detected, and optical flow images were obtained from the frames using the FarneBack method. The features of these obtained images were extracted by applying AlexNet, VGG16, MobilenetV2, EfficientNet, Squeezenet from CNN models. Then, combining the image features obtained from all CNN models. And then, the ones which are the most distinctive features were selected with the Particle Swarm Optimization (PSO) algorithm. The new feature set obtained was divided into classes positive, negative, and surprise using SVM. As a result, its success has been demonstrated with an accuracy rate of 0.8784 obtained in our proposed ME framework.
Açıklama
Anahtar Kelimeler
CNN, FarneBack, micro expression, optical flow, PSO, SVM
Kaynak
Traitement Du Signal
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
39
Sayı
5
