Pendeteksian Bagian Tubuh Manusia untuk Filter Pornografi dengan Metode Viola-Jones
DOI:
https://doi.org/10.21512/comtech.v3i1.2447Keywords:
detection, human body, Viola-Jones method, Haar-like features, boosting algorithmAbstract
Information Technology does help people to get information promptly anytime and anywhere. Unfortunately, the information gathered from the Internet does not always come out positive. Some information can be destructive, such as porn images. To mitigate this problem, the study aims to create a desktop application that could detect parts of human body which can be expanded in the future to become an image filter application for pornography. The detection methodology in this study is Viola-Jones method which provides a complete framework for extracting and recognizing image features. A combination of Viola-Jones method with Haar-like features, integral image, boosting algorithm, and cascade classifier provide a robust detector for the application. First, several parts of the human body are chosen to be detected as the data training using the Viola-Jones method. Then, another set of images (similar body parts but different images) are run through the application to be recognized. The result shows 86.25% of successful detection. The failures are identified and show that the inputted data are completely different with the data training.
Plum Analytics
References
Anna, Lusia Kus. (2011). Kekerasan Seksual Dipengaruhi Film Porno. Diakses Januari 2012, dari http://nasional.kompas.com/read/2011/01/03/13040246
Arihutomo, M. (2010). Rancangan Bangun Sistem Penjejakan Objek Menggunakan Metode Viola
Jones Untuk Aplikasi Eyebot. Proyek akhir. Institut Teknologi Sepuluh Nopember, Surabaya. Diakses dari http://www.eepis-its.edu/uploadta/downloadmk.php?id=1151
Barczak, Andre Luis Chautard. (2007). Feature-Based Rapid Object Detection: From Feature Extraction To Parallelisation. Tesis tidak diterbitkan. Massey University, Auckland, New Zealand. Diakses dari http://mro.massey.ac.nz/bitstream/handle/10179/742/02whole.pdf?sequence=1.
Dzulkamain, A. D., Dewantara, B. S., & Besari, A. A. (2011). Pengendalian Robot Lengan Beroda dengan Kamera Untuk Pengambilan Obyek. ITS, Surabaya.
Freund, Y., & Schapire, R. (2001). The Boosting Approach to Machine Learning An Overview. Diakses dari http://www.cs.princeton.edu/~schapire/uncompress-papers.cgi/msri.ps.
Gonzalez , R.C. & Woods , R.E. (2007). Digital Image Processing (3rd Edition). New Jersey: Prentice Hall. http://www.poskotanews.com/2012/01/19/perawan-siswi-sma-rp-700-ribu/
Kuranov, A., Lienhart, Rainer, & Pisarevsky, V. (2002.). An Empirical Analysis of Boosting Algorithms for Rapid Objects with an Extended Set of Haar-like Features. Intel Technical Report MRL-TR-July 02-01.
Lienhart, R., Kuranove, A., & Pisarevsky, V. (2002). Empirical analysis of detection cascades of boosted classifiers for rapid object detection. IEEE ICIP 2002, 1, 900-903.
Nugroho, S., & Harjoko, A. (2005). Penerapan Jaringan Syaraf Tiruan untuk Mendeteksi Posisi Wajah Manusia pada Citra Digtal. Program Pascasarjana Ilmu Komputer, Universitas Gadjah Mada, Yogyakarta.
Pornografi. (2008). Dalam Kamus Besar Bahasa Indonesia. Jakarta: Gramedia Pustaka Utama.
Pos Kota. (2012, Januari). Diakses Januari 2012 dari http://www.poskotanews.com/2012/01/19/korban-trafficking-dari-warga-miskin/;
Suprihadi, M. (2008). Terlibat Pornografi Anak, 121 Pria Ditangkap. Diakses dari http://nasional.kompas.com/read/2008/10/01/19491236/terlibat.pornografi.anak.121.pria.ditangkap
Syamsiar, T. F., Eru Puspita, S., & Budi Nur Iman, S. (2009). Identifikasi Scan Iris Mata Menggunakan Metode JST Propagasi Balik Untuk Aplikasi Sistem Pengamanan Brankas.
Viola, P., & Jones, M. (2001). Robust real-time object detection. Second International Workshop on Statistical and Computational Theories of Vision – Modeling, Learning, Computing, and Sampling. Vancouver, Canada.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License - Share Alike that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
USER RIGHTS
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options, currently being defined for this journal as follows:
