Implementasi Fitur Sift Dan Fisher Vector Encoding Untuk Aplikasi Pengenalan Wajah

Yudhantoro, Yusuf Azis Henny Tri (2017) Implementasi Fitur Sift Dan Fisher Vector Encoding Untuk Aplikasi Pengenalan Wajah. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Teknik pengenalan individu berbasis fitur biometrik wajah
menjadi salah satu teknik yang paling sering digunakan. Hal ini
dikarenakan penggunaan wajah untuk pengenalan memiliki
beberapa kelebihan, antara lain: pengenalan wajah hanya
membutuhkan peralatan kamera yang relatif ekonomis dan
algoritma yang baik mampu mengidentifikasi wajah.
Dalam Tugas Akhir ini diimplementasikan perangkat
lunak pengenalan wajah menggunakan Scalable Invariant Feature
Transform (SIFT) mode dense, dan fisher vector encoding, serta
mahalanobis metric learning. Hasil dari proses metric learning
adalah threshold kesamaan wajah dan bobot jarak fisher vector
pasangan gambar wajah yang berguna dalam proses klasifikasi.
Dataset yang digunakan dalam proses uji coba berisi
empat ratus pasangan gambar kelas positif dan negatif dari
Labeled Faces in the Wild (LFW). Akurasi terbaik sebesar 53%.
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Individual recognition techniques based on facial
biometric features is one of the most frequently used technique. due
to the usage of facial recognition has several advantages such as:
facial recognition process only need relatively economic camera
equipment and a good algorithm to be able to identify a face.
In this undergraduate theses, facial recognition software
using dense mode of Scalable Invariant Feature Transform (SIFT)
and fisher vector encoding to extract features. Weight of pair-faceimage’s
fisher vector and distance threshold to classify images
learned using mahalanobis metric learning.
The dataset used in the testing process contains four
hundred pairs of positive and negative class picture from Labeled
Faces in the Wild (LFW). The best accuration is 53%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Biometric, Pengenalan Wajah, SIFT, Fisher Vector, Mahalanobis Metric Learning
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing--Digital techniques
Divisions:	Faculty of Information Technology > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User:	- YUSUF AZIS HENNY TRI YUDHANTORO
Date Deposited:	07 Apr 2017 03:33
Last Modified:	27 Dec 2017 07:32
URI:	http://repository.its.ac.id/id/eprint/3168

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