@inproceedings{c552a3733cbb494fb57b007a2cd356c8,
title = "Nonlinear fuzzy robust PCA on shape modelling of active appearance model for facial expression recognition",
abstract = "Automatic facial expression recognition is one of the potential research area in the field of computer vison.It aims to improve the ability of machine to capture social signals in human.Automatic facial expression recognition is still a challenge. We proposed method using contrast limited adaptive histogram equalization (CLAHE) for pre-processing stage then performed feature extraction using active appearance model (AAM) based on nonlinear fuzzy robust principal component analysis (NFRPCA). The feature extraction results will be classified with support vector machine (SVM). Feature points generated AAM based on NFRPCA more adaptive compared to AAM based PCA.Our proposed method{\textquoteright}s the average accuracy rate reached 96,87% and 93,94% for six and seven basic emotions respectively.",
keywords = "AAM, Contrast limited adaptive histogramequalization, Facial expression, Nonlinear fuzzyrobust PCA, SVM",
author = "Nunik Pratiwi and T. Basaruddin and Widyanto, {M. Rahmat} and Liliana, {Dewi Yanti}",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 2017 International Conference on Video and Image Processing, ICVIP 2017 ; Conference date: 27-12-2017 Through 29-12-2017",
year = "2017",
month = dec,
day = "27",
doi = "10.1145/3177404.3177444",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "68--72",
booktitle = "Proceedings of 2017 International Conference on Video and Image Processing, ICVIP 2017",
address = "United States",
}