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All Journal Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika
Fahmi, Nofear
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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DETEKSI GERAK BERDASARKAN FITUR WAJAH MENGGUNAKAN METODE KANADE LUCAS TOMASI (KLT) Apridiansyah, Yovi; Marhalim; Fahmi, Nofear
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 2 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i2.19848

Abstract

Research by utilizing facial recognition features related to image processing and computer vision is used to produce a system that is almost close to the human visual system in general. In image processing, the detection of the movement of the rig is carried out so as to produce detection results. A problem that often occurs in the motion detection process is that every moving object in the video will be detected as a moving object. Therefore, this study will try to detect human face objects from the video data to be detected so that the detection results will later produce the detection of face objects. Every process of observing human facial movements requires a careful pre-process stage, because it is related to the observation of very smooth movements and a very fast duration. At this stage, the detection and tracking of the facial area must always be precise so that the observation of movements made in the facial area can be accurate. The solution offered for facial motion detection is to apply the Canade Lucas Tomasi (KLT) method for tracking each feature point. The performance process of KLT in detecting faces is to track each existing face by looking at the point of facial features, after the system records the features of the face, the system will detect every facial movement in the video. So by using the KLT method, it is hoped that the system can detect facial objects in the video. The results of the study by testing as many as 30 samples of video data in the form of recordings of human motion objects succeeded in detecting facial movements with an accuracy level of 96%, Recal 88% and an accuracy level of 86%.
Co-Authors Apridiansyah, Yovi Marhalim