Zufar, Muhammad (2016) Convolutional Neural Networks Untuk Pengenalan Wajah Secara Real-Time. Undergraduate thesis, Institut Technology Sepuluh Nopember.
Preview |
Text
1212100076-Undergraduate Thesis.pdf - Published Version Download (2MB) | Preview |
Abstract
Identifikasi identitas individu melalui pengenalan wajah
secara otomatis merupakan suatu persoalan besar yang menarik
dan banyak sekali berbagai macam pendekatan untuk
menyelesaikan persoalan ini. Apalagi di dalam skenario
kehidupan nyata yang tidak terkontrol, wajah akan terlihat dari
berbagai sisi dan tidak selalu menghadap ke depan yang membuat
permasalahan klasifikasi menjadi lebih sulit diselesaikan. Dalam
Tugas Akhir ini digunakan salah satu metode deep neural
networks yaitu Convolutional Neural Networks (CNN) sebagai
pengenalan wajah secara real-time yang sudah terbukti sangat
efisien dalam klasifikasi wajah. Metode diimplementasikan
dengan bantuan library OpenCV untuk deteksi multi wajah dan
perangkat Web Cam M-Tech 5MP. Dalam penyusunan arsitekur
model Convolutional Neural Networks dilakukan konfigurasi
inisialisasi parameter untuk mempercepat proses training
jaringan. Hasil uji coba dengan munggunakan konstruksi model
Convolutional Neural Networks sampai kedalaman 7 lapisan
dengan input dari hasil ekstraksi Extended Local Binary Pattern
dengan radius 1 dan neighbor 15 menunjukkan kinerja
pengenalan wajah meraih rata-rata tingkat akurasi lebih dari 89%
dalam ∓ 2 frame per detik.
=======================================================================================================
Identification of individual identity through facial
recognition automatically is a huge deal of interest and a lot of
the various approaches to resolving the problems. Moreover, in
real life scenarios that are not controlled, the face will be visible
from all sides and not always facing the front which makes it
more difficult classification problems resolved. This final project
use one of the methods of deep neural networks that
Convolutional Neural Networks (CNN) as face recognition in
real-time which has proven very efficient in the face
classification. The method is implemented with the help of
OpenCV library for multi face detection devices and a 5MP MTech
Web Cam. In the preparation of architectural models
Convolutional Neural Networks do initial configuration
parameters to speed up the training process of the network. The
trial results with munggunakan model construction Convolutional
Neural Networks to a depth of 7 layers with input from the
extraction Extended Local Binary Pattern with radius 1 and
neighbor 15 shows the performance of face recognition reached
average accuracy rate of more than 89% in ∓ 2 frames per
second.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSMa 006.33 Zuf c-1 |
| Uncontrolled Keywords: | Pengenalan Wajah, Real-Time, Convolutional Neural Networks |
| Subjects: | Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) |
| Divisions: | Faculty of Mathematics and Science > Mathematics > 44201-(S1) Undergraduate Thesis |
| Depositing User: | Mr. Tondo Indra Nyata |
| Date Deposited: | 06 Jan 2020 05:56 |
| Last Modified: | 06 Jan 2020 05:56 |
| URI: | http://repository.its.ac.id/id/eprint/72552 |
Actions (login required)
 |
View Item |