Dataset classification: An efficient feature extraction approach for grammatical facial expression recognition

被引：2

作者：

Aleesa, Rula Sami ^{[1
,2
]}

Mohammadi, Hossein Mahvash ^{[1
]}

Monadjemi, Amirhassan ^{[1
,3
]}

Hashim, Ivan A. ^{[4
]}

机构：

[1] Univ Isfahan, Dept Comp Engn, Esfahan, Iran

[2] Univ Babylon, Fac Mat Engn, Babylon, Iraq

[3] Natl Univ Singapore, Sch Comp, Singapore, Singapore

[4] Univ Technol Baghdad, Dept Elect Engn, Baghdad, Iraq

来源：

COMPUTERS & ELECTRICAL ENGINEERING | 2023年 / 110卷

关键词：

GFEs; Facial expressions; Facial action coding system; Eye-gaze; Features extracting; HEAD POSE;

D O I：

10.1016/j.compeleceng.2023.108891

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, an efficient features extraction using validated statistical approaches is proposed, along with a robust Grammatical Facial Expressions (GFEs) classifier in facial expression recognition systems. Accordingly, a new dataset was collected from 70 participants (33 males and 37 females) ranging in age from 18 to 46. The total number of video clips collected was 765.The features extracted in this study consist of 17 features associated with three categories of non-manual features: facial expression, head movement, and eye-gaze. Automatic recognition of nine classes of grammatical facial expressions in two languages (Arabic and Persian) is performed using a linear Support Vector Machine (SVM) classifier.The proposed system was also validated by testing it on the American Sign Language (ASL) dataset. In comparison to previous works on the ASL dataset, the results showed a higher accuracy rate of 95%.

引用

页数：14