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ITS » Paper and Presentation » Teknik Elektro S2
Posted by tondoindra@gmail.com at 23/12/2014 18:54:50  •  1090 Views


EKSTRAKSI FITUR BERBASIS WAVELET UNTUK PENGENALAN EKSPRESI WAJAH

WAVELET-BASE FEATURE EXTRACTION FOR FACIAL EXPRESSION RECOGNITION

Author :
PRASETYO, JAROT DWI ( 2210205204 )




ABSTRAK

Ekspresi wajah adalah bagian yang terpenting dalam berkomunikasi antar manusia dan dapat di aplikasikan dalam aplikasi yang nyata seperti interaksi antara manusia dan komputer kontrol robot dan aplikasi lainnya. Oleh karena itu untuk memfasilitasi antarmuka manusia dengan mesin yang lebih bersahabat pada produk multimedia maka pengenalan ekspresi wajah pada antarmukan sangat membantu dalam kenyamanan berinteraksi.Salah satu langkah yang berpengaruh dalam pengenalan ekspresi wajah adalah ketepatan ekstrasi pada fitur wajah. Beberapa pendekatan dalam pengenalan ekspresi wajah pada ekstrasi fiturnya tidak mempertimbangkan dimensi dari data fitur sebagai inputan dari mesin pembelajaran Melalui penelitian ini mengusulkan sebuah algoritma wavelet yang digunaka untuk memperkecil dari dimensi data fitur. Data fitur ini kemudian diklasifikasikan menggunakan mesin pembelajaran SVM-multiclass untuk menentukan perbedaan dari enam ekspresi wajah yaitu marah benci takut bahagia sedih dan terkejut yang terdapat pada JAFFE database. Menghasilkan keakurasian klasifikasi sebesar 83.25 dari 208 data sampel.


ABSTRACT

Facial expressions are an essential part of communication between people and can be applied in real applications such as interaction between humans and computers robot control and other applications. Therefore to facilitate human machine interface more friendly on multimedia products the facial expression recognition on antarmukan very helpful in interacting comfort.One of the steps that affect the facial expression recognition is the extraction accuracy in facial features. Several approaches to facial expression recognition on its extraction does not take into consideration the dimensions of the data as input features of machine learning. Through this research proposes a wavelet algorithm to minimize digunaka of feature data dimension. Feature data is then classified using SVM-multiclass machine learning to determine the difference of six facial expressions as anger hate fear happy sad and shocked contained in the database JAFFE. Produce a classification accuracy of 83.25 of the 208 sample data.



Keywordswavelet, SVM, Pengenalan ekspresi wajah, Interaksi komputer manusia.
 
Subject:  Pengawasan komputer
Contributor
  1. Dr. SURYA SUMPENO, ST, M.Sc.
Date Create: 28/08/2013
Type: Text
Format: PDF
Language: Indonesian
Identifier: ITS-paper-22121140006668
Collection ID: 22121140006668
Call Number: RTE 006.42 Pra ep


Source
Paper And Presentation of Electrical Engineeing RTE 006.42 Pra ep, 2014

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Copyright @2013 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library




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ITS-paper-22121140006668-35028.pdf




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