@inproceedings{9e6d45b1320347f9be2571982fb69e16,
title = "Enhancing facial component analysis",
abstract = "Capturing non-verbal information from a face is the most important phase in emotion recognition through facial expression. In order to do that, it needs to find suitable features extraction method that could help to figure out the meaning from the given face. Inspired by Liliana Dewi[1] that used a geometric feature for analyzing facial component with a good result accuracy around 98.59%. So this paper tries to improve that method in order to enhance the result of facial components analysis. This study use Hellen dataset to retrain the Active Appearance Model (AAM), add image preprocessing like image enhancement, use other geometric features and Fuzzy Rule-Based System for extract the feature on extended Cohn Kanade (CK+) dataset. The result of this research gives better in recognizing facial components than previous work with accuracy around 99.36%.",
keywords = "Aam, Active appearance model, Facial component analysis, Fuzzy rule based system",
author = "Siska Pebiana and T. Basaruddin and Widyanto, {M. Rahmat} and Liliana, {Dewi Yanti}",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 2nd International Conference on Software Engineering and Information Management, ICSIM 2019 - and its Workshop 2019 2nd International Conference on Big Data and Smart Computing, ICBDSC 2019 ; Conference date: 10-01-2019 Through 13-01-2019",
year = "2019",
month = jan,
day = "10",
doi = "10.1145/3305160.3305174",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "175--179",
booktitle = "Proceedings of the 2019 2nd International Conference on Software Engineering and Information Management, ICSIM 2019 - Workshop 2019 2nd International Conference on Big Data and Smart Computing, ICBDSC 2019",
address = "United States",
}