IMPLEMENTATION SUPPORT VECTOR MACHINE (SVM) AND HISTOGRAM OF ORIENTED GRADIENTS (HOG) FOR REAL TIME FACE RECOGNITION APPLICATION

KAMAL, MUHAMAD IHRAM RASHKY (2023) IMPLEMENTATION SUPPORT VECTOR MACHINE (SVM) AND HISTOGRAM OF ORIENTED GRADIENTS (HOG) FOR REAL TIME FACE RECOGNITION APPLICATION. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The first and most important stage in determining whether there are faces in an image is face detection. The next step involves face verification or recognition, which determines whether a face-containing test input passes matches matching faces already in the database or face gallery. To create low-dimensional representations of any face, Open Face is Core offers a feature extraction technique. Run the application to see a demonstration of how a face classifier can be made using these representations. There is a difference between developing an SVM a feature representation model, developing an SVM formula for categorizing individuals. Suggested approach is capable of real-time facial recognition and can identify faces from the input photographs provided. Even if there are multiple faces present in the input photos, it can still detect faces. Keywords: Face Detection, Face Recognition, SVM, HOG Tahap pertama dan terpenting dalam menentukan apakah ada wajah dalam sebuah gambar adalah deteksi wajah. Langkah selanjutnya melibatkan verifikasi atau pengenalan wajah, yang menentukan apakah input tes yang mengandung wajah cocok dengan wajah yang sudah ada di database atau galeri wajah. Untuk membuat representasi dimensi rendah dari wajah apa pun, Open Face is Core menawarkan teknik ekstraksi fitur. Jalankan aplikasi untuk melihat demonstrasi bagaimana pengklasifikasi wajah dapat dibuat menggunakan representasi ini. Ada perbedaan antara mengembangkan SVM model representasi fitur, mengembangkan formula SVM untuk mengkategorikan individu. Pendekatan yang disarankan mampu pengenalan wajah secara real-time dan dapat mengidentifikasi wajah dari foto input yang diberikan. Bahkan jika ada beberapa wajah yang ada di foto input, itu masih dapat mendeteksi wajah. Kata Kunci : Deteksi Wajah, Pengenalan Wajah, SVM, HOG

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 23 023
Call Number:	SIK/15/23/025
NIM/NIDN Creators:	41519010075
Uncontrolled Keywords:	Deteksi Wajah, Pengenalan Wajah, SVM, HOG
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	ADELINA HASNA SETIAWATI
Date Deposited:	04 Apr 2023 03:53
Last Modified:	04 Apr 2023 03:53
URI:	http://repository.mercubuana.ac.id/id/eprint/76024

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