Facial Expression Recognition with an Optimized Radial Basis Kernel
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this work, a new approach for facial expression recognition has been proposed. The approach has imbedded in it both new feature extraction technique and classification techniques using automatic auto-tuning of kernel parameter optimization in support vector machines. It generally begins with feature extraction from the input vectors using a combination of arithmetic means difference and rotation invariant Local Binary Pattern. The extracted features are projected into a Gaussian space to match it with the radial basis function kernel used in support vector machines for classification. Prior to classification, an optimized parameter for support vector machines training are automatically determined based on an approach proposed which relies on the receiver operating characteristics of the support vector machine classifier. The results obtained from the experiments were impressive and promising. From the experiments conducted on the two facial expression databases with different cross-validation techniques, the proposed approach outperforms its counterparts under the same database and settings.
Açıklama
6th International Symposium on Digital Forensic and Security (ISDFS) -- MAR 22-25, 2018 -- Antalya, TURKEY
Anahtar Kelimeler
Facaial Expression Recognition, Radial Basis Function, Support vector Machine, arithmatic mean difference, rotation invariant LBP
Kaynak
2018 6TH INTERNATIONAL SYMPOSIUM ON DIGITAL FORENSIC AND SECURITY (ISDFS)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
