DocumentCode
3660161
Title
Facial memorability prediction fusing geometric and texture features
Author
Ziyi Dai;Zehua Pan;Yewei Wu;Linlin Shen;Qibin Hou
Author_Institution
College of Computer Science &
fYear
2015
Firstpage
998
Lastpage
1002
Abstract
As different faces have different features, the degree of memorability of faces are different, which are named memorability in this paper. We mainly study the relation between the memorability and different features such as the geometrical features of the faces, the location of eyes, the size of mouth and eyes and the Histogram of Oriented Gradient (HOG). We use SVR model to regress the features of face images, and predict the memorability score. Finally, we use the spearman rank correlation coefficient and residual sum-of-squares error to analyze the correlation and error of the predicted memorability score with ground truth.
Keywords
"Mouth","Feature extraction","Computer vision","Shape","Correlation","Conferences","Predictive models"
Publisher
ieee
Conference_Titel
Information and Automation, 2015 IEEE International Conference on
Type
conf
DOI
10.1109/ICInfA.2015.7279432
Filename
7279432
Link To Document