@inproceedings{731b58f0d656436392331699da9cbe15,
title = "Facial memorability prediction fusing geometric and texture features",
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 sumof- squares error to analyze the correlation and error of the predicted memorability score with ground truth.",
keywords = "SVR, memorability, multiple features",
author = "Ziyi Dai and Zehua Pan and Yewei Wu and Linlin Shen and Qibin Hou",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics ; Conference date: 08-08-2015 Through 10-08-2015",
year = "2015",
month = sep,
day = "28",
doi = "10.1109/ICInfA.2015.7279432",
language = "English",
series = "2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "998--1002",
booktitle = "2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics",
address = "United States",
}